{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config IPCompleter.greedy = True\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline\n",
    "%load_ext tensorboard\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "import tensorflow as tf\n",
    "import os\n",
    "from datetime import datetime\n",
    "import tensorflow as tf\n",
    "import sklearn\n",
    "import random\n",
    "import operator\n",
    "\n",
    "pd.set_option('mode.chained_assignment', None)\n",
    "sn.set(rc={'figure.figsize':(9,9)})\n",
    "sn.set(font_scale=1.4)\n",
    "\n",
    "# make results reproducible\n",
    "seed = 0\n",
    "np.random.seed(seed)\n",
    "tf.random.set_seed(13)"
   ]
  },
  {
   "attachments": {
    "agent2.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reinforcement Learning\n",
    "\n",
    "Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.\n",
    "\n",
    "Reinforcement learning differs from supervised learning in not needing labelled input/output pairs be presented, and learning to act under evaluative feedback (rewards). Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).\n",
    "\n",
    "The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms (that involve ANNs) is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.\n",
    "\n",
    "---\n",
    "\n",
    "Reinforcement learning is *Agent-oriented learning*, that is learning by interacting with an environment to achieve a goal. Which is learning by trial and error, with only delayed evaluative feedback (reward).\n",
    "\n",
    "However RL does have some challenges:\n",
    "* We only have a reward signal as feedback\n",
    "* Feedback is often delayed\n",
    "* Time matters, sequential and non-stationary data\n",
    "* Data received is affected by the agents previous interactions\n",
    "\n",
    "It has been applied successfully to various problems, including robot control, power control, telecommunications, backgammon, checkers and Go.\n",
    "\n",
    "## Problem formulation as a Markov Decision Process (MDP)\n",
    "\n",
    "Often we consider the problem of making a sequence of good decisions. That is in a discrete setting, an agent will make sequence of **actions** $\\{a_t\\}$, observe a sequence of **observations** $\\{o_t\\}$ and receive a sequence of **rewards** $\\{r_t\\}$. We define the **history** at time $t$ to be $h_t = (a_1, o_1, r_1, \\dots, a_t, o_t, r_t)$. The agent's function of the history, that is, $a_{t+1} = f(h_t)$, and the problem of sequential decision making can be thought of as defining and computing the function $f$ appropriately.\n",
    "\n",
    "![agent2.png](attachment:agent2.png)\n",
    "\n",
    "\n",
    "At each step $t$ the agent (as illustrated above) executes action $a_t$, receives observation $o_t$ and receives a scalar reward $r_t$. The environment receives action $a_t$, emits observation $o_{t+1}$ and emits a scalar reward $r_{t+1}$.\n",
    "\n",
    "Our world may be in a state of possible states ($S$), however the agent observes a sequence of states $\\{s_t\\}$. We often want to consider the **transition dynamics** of the world $P(s_{t+1}|s_t,a_t,\\dots,s_1,a_1)$, a probability distribution over $S$ conditioned on the previous states and actions. In RL, we often assume the **Markov property** that\n",
    "\n",
    "$$ P(s_{t+1}|s_t,a_t,\\dots,s_1,a_1)=P(s_{t+1}|s_t,a_t) $$\n",
    "\n",
    "We can use the trick to make sure the Markov property holds, by using the history $h_t$ as our state. Usually, we consider the reward $r_t$ to be received on the transition between states, $s_t \\xrightarrow{a_t} s_{t+1}$. A reward function is used to predict rewards, $R(s, a, s′) = \\mathop{\\mathbb{E}}[r_t|s_t = s, a_t = a, s_{t+1} = s′]$. We will often consider the reward function to be of the form $R(s) = \\mathop{\\mathbb{E}}[r_t|s_t = s]$ or $R(s, a) = \\mathop{\\mathbb{E}}[r_t|s_t = s, a_t = a]$. A **model** consists of the above transition dynamics and reward function.\n",
    "\n",
    "The **agent state** is a function of the history, $s^a_t = g(h_t)$, a RL agent typically has an explicit representation of one or more of the following three things, a policy, a value function and optionally a model. A **policy** $\\pi$ is a mapping from the agent state to an action, $\\pi(s^a_t)\\in A$,\n",
    "or, sometimes it is a stochastic distribution over actions $\\pi(a_t|s^a_t)$. When the agent wants to take an action and $\\pi$ is stochastic, it picks action $a \\in A$ with probability $P(a_t = a) = \\pi(a|s^a_t)$. Given a policy $\\pi$ and discount factor $\\gamma \\in [0, 1]$, a **value function** $V^{\\pi}$ is an expected sum of discounted rewards (e.g. how good is each state, can also include action),\n",
    "\n",
    "\n",
    "$$ V^\\pi(s) = \\mathop{\\mathbb{E}_\\pi}[r_t + \\gamma r_{t+1} + \\gamma^2 r_{t+2} + \\dots | s_t = s]$$\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Where $\\mathop{\\mathbb{E}_\\pi}$ denotes that the expectation is taken over states encountered by following policy $\\pi$, and, the discount factor $\\gamma$ is used to weigh immediate rewards versus delayed rewards. Lastly, if the agent has a model, we would call it a **model-based** agent, and if it doesn't incorporate a model (agent's representation of the environment), we would call it a **model-free** agent.\n",
    "\n",
    "For the case where $o_t\\neq s_t$ **partially observable**, and it is common in partially observable settings for RL algorithms to maintain a probability distribution over the true world state to define $s^a_t$ , which is known as a **belief state**. However for majority of cases we will consider the fully observable case, where $o_t = s_t$, and will assume that $s^a_t = s_t$. [[1](https://web.stanford.edu/class/cs234/slides/lnotes_intro.pdf)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Markov Decision Process (MDP)\n",
    "\n",
    "Here a Markov decision process (MDP) is a discrete time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. It is fully defined by the tuple of $(S, A, P, R, \\gamma)$:\n",
    "* $S$ Set of all possible states\n",
    "* $A$ Set of all possible actions\n",
    "* $R$ distribution of reward given (state, action) pair\n",
    "* $P$ transition probability (of the world) i.e. distribution over next state given (state, action) pair\n",
    "* $\\gamma$ discount factor\n",
    "\n",
    "The core problem of MDPs is to find a **policy** for the agent: a function $\\pi$ that specifies the action $\\pi(s)$ that the agent will choose when in state $s$. Once a Markov decision process is combined with a policy in this way, this fixes the action for each state and the resulting combination behaves like a Markov chain (since the action chosen in state $s$ is completely determined by $\\pi(s)$ and $\\Pr(s_{t+1}=s' \\mid s_t = s, a_t=a)$ reduces to $\\Pr(s_{t+1}=s' \\mid s_t = s)$, a Markov transition matrix).\n",
    "\n",
    "The goal is to choose a policy $\\pi$ that will maximize some cumulative function of the random rewards, typically the expected discounted sum over a potentially infinite horizon. Hence the optimal policy $\\pi^*$ is given by:\n",
    "\n",
    "$$\\pi^* = \\operatorname{arg max}_{\\pi} \\mathop{\\mathbb{E}}[\\sum^{\\infty}_{t=0} {\\gamma^t R (s_t, a_t, s_{t+1})}] $$\n",
    "\n",
    "$$ V^*(s) = \\operatorname{arg max}_\\pi V_{\\pi^*}(s)$$\n",
    "\n",
    "(where we choose $a_t = \\pi(s_t)$, i.e. actions given by the policy). And the expectation is taken over $s_{t+1} \\sim P_{a_t}(s_t,s_{t+1})$\n",
    "\n",
    "where $\\ \\gamma \\ $ is the discount factor satisfying $0 \\le\\ \\gamma\\ \\le\\ 1$, which is usually close to 1.\n",
    "\n",
    "Because of the Markov property, the optimal policy for this particular problem can indeed be written as a function of $s$ only, as assumed above.\n",
    "\n",
    "The discount-factor motivates the agent to favor taking actions early, rather not postpone them indefinitely.\n",
    "\n",
    "This optimal policy can be found through a variety of methods, like dynamic programming. Some solutions require knowledge of the state transition function $P$ (**model**) and the reward function $R$. Others can solve for the optimal policy of an MDP using experimentation alone. \n",
    "\n",
    "The standard family of algorithms to calculate this optimal policy requires storage for two arrays indexed by state: **value** $V$, which contains real values, and **policy** $\\pi$, which contains actions. At the end of the algorithm, $\\pi$ will contain the solution and $V(s)$ will contain the discounted sum of the rewards to be earned (on average) by following that solution from state $s$.\n",
    "\n",
    "The algorithm has two steps, (1) a value update and (2) a policy update, which are repeated in some order for all the states until no further changes take place.  Both recursively update \n",
    "a new estimation of the optimal policy and state value using an older estimation of those values (where $s'=s_{t+1}$).\n",
    "\n",
    "$$V(s) := \\sum_{s'} P_{\\pi(s)} (s,s') \\left( R_{\\pi(s)} (s,s') + \\gamma V(s') \\right)$$\n",
    "\n",
    "$$\\pi(s) := \\operatorname{argmax}_a \\left\\{ \\sum_{s'} P(s' \\mid s, a) \\left( R(s'\\mid s,a) + \\gamma V(s') \\right) \\right\\} $$\n",
    "\n",
    "Their order depends on the variant of the algorithm; one can also do them for all states at once or state by state, and more often to some states than others. As long as no state is permanently excluded from either of the steps, the algorithm will eventually arrive at the correct solution. Some of variants are:\n",
    "\n",
    "## Value iteration\n",
    "\n",
    "In value iteration, which is also called backward induction,\n",
    "the $\\pi$ function is not used; instead, the value of $\\pi(s)$ is calculated within $V(s)$ whenever it is needed. Substituting the calculation of $\\pi(s)$ into the calculation of $V(s)$ gives the combined\n",
    "\n",
    "$$ V_{i+1}(s) := \\max_a \\left\\{ \\sum_{s'} P(s,a,s') \\left( R(s,a,s') + \\gamma V_i(s') \\right) \\right\\}, $$\n",
    "\n",
    "where $i$ is the iteration number. Value iteration starts at $i = 0$ and $V_0$ as a guess of the value function. It then iterates, repeatedly computing $V_{i+1}$ for all states $s$, until $V$ converges with the left-hand side equal to the right-hand side (which is the *Bellman equation* for this problem).\n",
    "\n",
    "## Policy iteration\n",
    "\n",
    "In policy iteration, step one is performed once, and then step two is repeated until it converges. Then step one is again performed once and so on.\n",
    "\n",
    "Instead of repeating step two to convergence, it may be formulated and solved as a set of linear equations. These equations are merely obtained by making $s = s'$ in the step two equation. Thus, repeating step two to convergence can be interpreted as solving the linear equations by Relaxation (iterative method).\n",
    "\n",
    "This variant has the advantage that there is a definite stopping condition: when the array $\\pi$ does not change in the course of applying step 1 to all states, the algorithm is completed.\n",
    "\n",
    "Policy iteration is usually slower than value iteration for a large number of possible states. It takes $O(n^3)$ time to solve for $n$ states, making impractical for large state spaces.\n",
    "\n",
    "\n",
    "Lets illustrate these with a MDP example, we can first create a MDP class\n",
    "\n",
    "# MDP example\n",
    "\n",
    "[[2](http://aima.cs.berkeley.edu/)]\n",
    "[[3](https://github.com/aimacode/aima-python/blob/master/mdp.ipynb)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BaseMDP:\n",
    "    \"\"\"A Markov Decision Process, defined by an initial state, transition model,\n",
    "    and reward function. We also keep track of a gamma value, for use by\n",
    "    algorithms. The transition model is represented somewhat differently from\n",
    "    the text. Instead of P(s' | s, a) being a probability number for each\n",
    "    state/state/action triplet, we instead have T(s, a) return a\n",
    "    list of (p, s') pairs. We also keep track of the possible states,\n",
    "    terminal states, and actions for each state. \"\"\"\n",
    "\n",
    "    def __init__(self, init, actlist, terminals, transitions=None, reward=None, states=None, gamma=0.9):\n",
    "        if not (0 < gamma <= 1):\n",
    "            raise ValueError(\"An MDP must have 0 < gamma <= 1\")\n",
    "\n",
    "        # collect states from transitions table if not passed.\n",
    "        self.states = states or self.get_states_from_transitions(transitions)\n",
    "\n",
    "        self.init = init\n",
    "\n",
    "        if isinstance(actlist, list):\n",
    "            # if actlist is a list, all states have the same actions\n",
    "            self.actlist = actlist\n",
    "\n",
    "        elif isinstance(actlist, dict):\n",
    "            # if actlist is a dict, different actions for each state\n",
    "            self.actlist = actlist\n",
    "\n",
    "        self.terminals = terminals\n",
    "        self.transitions = transitions or {}\n",
    "        if not self.transitions:\n",
    "            print(\"Warning: Transition table is empty.\")\n",
    "\n",
    "        self.gamma = gamma\n",
    "\n",
    "        self.reward = reward or {s: 0 for s in self.states}\n",
    "\n",
    "        # self.check_consistency()\n",
    "\n",
    "    def R(self, state):\n",
    "        \"\"\"Return a numeric reward for this state.\"\"\"\n",
    "\n",
    "        return self.reward[state]\n",
    "\n",
    "    def T(self, state, action):\n",
    "        \"\"\"Transition model. From a state and an action, return a list\n",
    "        of (probability, result-state) pairs.\"\"\"\n",
    "\n",
    "        if not self.transitions:\n",
    "            raise ValueError(\"Transition model is missing\")\n",
    "        else:\n",
    "            return self.transitions[state][action]\n",
    "\n",
    "    def actions(self, state):\n",
    "        \"\"\"Return a list of actions that can be performed in this state. By default, a\n",
    "        fixed list of actions, except for terminal states. Override this\n",
    "        method if you need to specialize by state.\"\"\"\n",
    "\n",
    "        if state in self.terminals:\n",
    "            return [None]\n",
    "        else:\n",
    "            return self.actlist\n",
    "\n",
    "    def get_states_from_transitions(self, transitions):\n",
    "        if isinstance(transitions, dict):\n",
    "            s1 = set(transitions.keys())\n",
    "            s2 = set(tr[1] for actions in transitions.values()\n",
    "                     for effects in actions.values()\n",
    "                     for tr in effects)\n",
    "            return s1.union(s2)\n",
    "        else:\n",
    "            print('Could not retrieve states from transitions')\n",
    "            return None\n",
    "\n",
    "    def check_consistency(self):\n",
    "\n",
    "        # check that all states in transitions are valid\n",
    "        assert set(self.states) == self.get_states_from_transitions(self.transitions)\n",
    "\n",
    "        # check that init is a valid state\n",
    "        assert self.init in self.states\n",
    "\n",
    "        # check reward for each state\n",
    "        assert set(self.reward.keys()) == set(self.states)\n",
    "\n",
    "        # check that all terminals are valid states\n",
    "        assert all(t in self.states for t in self.terminals)\n",
    "\n",
    "        # check that probability distributions for all actions sum to 1\n",
    "        for s1, actions in self.transitions.items():\n",
    "            for a in actions.keys():\n",
    "                s = 0\n",
    "                for o in actions[a]:\n",
    "                    s += o[0]\n",
    "                assert abs(s - 1) < 0.001\n",
    "\n",
    "        \n",
    "class MDP(BaseMDP):\n",
    "    def __init__(self, init, terminals, transition_matrix, reward = None, gamma=.9):\n",
    "        # All possible actions.\n",
    "        actlist = []\n",
    "        for state in transition_matrix.keys():\n",
    "            actlist.extend(transition_matrix[state])\n",
    "        actlist = list(set(actlist))\n",
    "        BaseMDP.__init__(self, init, actlist, terminals, transition_matrix, reward, gamma=gamma)\n",
    "\n",
    "    def T(self, state, action):\n",
    "        if action is None:\n",
    "            return [(0.0, state)]\n",
    "        else: \n",
    "            return self.transitions[state][action]"
   ]
  },
  {
   "attachments": {
    "mdp-1.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets implement the simple MDP in the image below. States A, B have actions X, Y available in them. Their probabilities are shown just above the arrows. We start with using BaseMDP as base class for our CustomMDP. Obviously we need to make a few changes to suit our case. We make use of a transition matrix as our transitions are not very simple.\n",
    "\n",
    "![mdp-1.png](attachment:mdp-1.png)\n",
    "[[4](https://github.com/aimacode/aima-python/blob/master/mdp.ipynb)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transition Matrix as nested dict. State -> Actions in state -> List of (Probability, State) tuples\n",
    "t = {\n",
    "    \"A\": {\n",
    "            \"X\": [(0.3, \"A\"), (0.7, \"B\")],\n",
    "            \"Y\": [(1.0, \"A\")]\n",
    "         },\n",
    "    \"B\": {\n",
    "            \"X\": {(0.8, \"End\"), (0.2, \"B\")},\n",
    "            \"Y\": {(1.0, \"A\")}\n",
    "         },\n",
    "    \"End\": {}\n",
    "}\n",
    "\n",
    "init = \"A\"\n",
    "\n",
    "terminals = [\"End\"]\n",
    "\n",
    "rewards = {\n",
    "    \"A\": 5,\n",
    "    \"B\": -10,\n",
    "    \"End\": 100\n",
    "}\n",
    "\n",
    "mdp_1 = MDP(init, terminals, t, rewards, gamma=.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Value Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_iteration(mdp, epsilon=0.001):\n",
    "    \"\"\"Solving an MDP by value iteration\"\"\"\n",
    "    history = []\n",
    "    V1 = {s: 0 for s in mdp.states}\n",
    "    R, T, gamma = mdp.R, mdp.T, mdp.gamma\n",
    "    while True:\n",
    "        V = V1.copy()\n",
    "        history.append(V)\n",
    "        delta = 0\n",
    "        for s in mdp.states:\n",
    "            V1[s] = R(s) + gamma * max(sum(p * V[s1] for (p, s1) in T(s, a))\n",
    "                                       for a in mdp.actions(s))\n",
    "            delta = max(delta, abs(V1[s] - V[s]))\n",
    "        if delta <= epsilon * (1 - gamma) / gamma:\n",
    "            return V, pd.DataFrame(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': 72.10147625879485, 'B': 75.60975599852492, 'End': 100.0}\n"
     ]
    }
   ],
   "source": [
    "V, history = value_iteration(mdp_1)\n",
    "print(V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 546,
       "width": 571
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history.plot()\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('State Value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Policy Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expected_value(a, s, V, mdp):\n",
    "    \"\"\"The expected value of doing a in state s, according to the MDP and V.\"\"\"\n",
    "\n",
    "    return sum(p * V[s1] for (p, s1) in mdp.T(s, a))\n",
    "\n",
    "def policy_evaluation(pi, V, mdp, k=20):\n",
    "    \"\"\"Return an updated value mapping V from each state in the MDP to its\n",
    "    value, using an approximation (modified policy iteration).\"\"\"\n",
    "\n",
    "    R, T, gamma = mdp.R, mdp.T, mdp.gamma\n",
    "    for i in range(k):\n",
    "        for s in mdp.states:\n",
    "            V[s] = R(s) + gamma * sum(p * V[s1] for (p, s1) in T(s, pi[s]))\n",
    "    return V\n",
    "\n",
    "def policy_iteration(mdp):\n",
    "    \"\"\"Solve an MDP by policy iteration\"\"\"\n",
    "\n",
    "    history = []\n",
    "    V = {s: 0 for s in mdp.states}\n",
    "    pi = {s: random.choice(mdp.actions(s)) for s in mdp.states}\n",
    "    while True:\n",
    "        V = policy_evaluation(pi, V, mdp)\n",
    "        history.append(V)\n",
    "        unchanged = True\n",
    "        for s in mdp.states:\n",
    "            a = max(mdp.actions(s), key=lambda a: expected_value(a, s, V, mdp))\n",
    "            if a != pi[s]:\n",
    "                pi[s] = a\n",
    "                unchanged = False\n",
    "        if unchanged:\n",
    "            return pi, V, pd.DataFrame(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value Function: \n",
      " {'A': 72.10157032031375, 'B': 75.60975609756038, 'End': 100.0}\n",
      "\n",
      "Policy: \n",
      " {'A': 'X', 'B': 'X', 'End': None}\n"
     ]
    }
   ],
   "source": [
    "pi, V, history = policy_iteration(mdp_1)\n",
    "print('Value Function: \\n', V)\n",
    "print('\\nPolicy: \\n', pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reinforcement Learning"
   ]
  },
  {
   "attachments": {
    "grid_mdp.jpg": {
     "image/jpeg": "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"
    },
    "grid_mdp_agent.jpg": {
     "image/jpeg": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Passive Reinforcement Learning\n",
    "\n",
    "For Passive Reinforcement Learning the agent follows a fixed policy $\\pi$. It attempts to evaluate a given policy $\\pi$ - without any knowledge of the Reward function $R(s)$ and the Transition model $P(s_{t+1}|s_t,a_t)$.\n",
    "\n",
    "This is achieved by **value estimation**, where the agent attempts to directly learn the value of each state that would result from following the policy. Although at each step, it has to *perceive* the reward and the state - it has no global knowledge of these. Thus, if a certain set of actions offers a very low probability of attaining some state $s_+$ - the agent may never perceive the reward $R(s_+)$.\n",
    "\n",
    "For a series of actions given by $\\pi$, the estimated value $V$:\n",
    "\n",
    "$$V^{\\pi}(s) = \\mathop{\\mathbb{E}_\\pi}[\\sum_{t=0}^\\infty \\gamma^t R^t(s')]$$\n",
    "\n",
    "Or the expected value of summed discounted rewards until termination.\n",
    "\n",
    "Based on this concept, we discuss three methods of estimating the value:\n",
    "\n",
    "1. **Direct Value Estimation (DVE)**\n",
    " \n",
    " The first, most naive method of estimating value comes from the simplest interpretation of the above definition. We construct an agent that follows the policy until it reaches the terminal state. At each step, it logs its current state, reward. Once it reaches the terminal state, it can estimate the value for each state for *that* iteration, by simply summing the discounted rewards from that state to the terminal one.\n",
    "\n",
    " It can now run this *simulation* $n$ times, and calculate the average value of each state. If a state occurs more than once in a simulation, both its value values are counted separately.\n",
    " \n",
    " Note that this method may be prohibitively slow for very large state spaces. Besides, **it pays no attention to the transition probability model $P(s_{t+1}|s_t,a_t)$.** It misses out on information that it is capable of collecting (say, by recording the number of times an action from one state led to another state). The next method addresses this issue.\n",
    " \n",
    "2. **Adaptive Dynamic Programming (ADP)**\n",
    " \n",
    " This method makes use of knowledge of the past state $s_t$, the action $a_t$, and the new perceived state $s_{t+1}$ to estimate the transition probability model $P(s_{t+1}|s_t,a_t)$. It does this by the simple counting of new states resulting from previous states and actions.\n",
    " The program runs through the policy a number of times, keeping track of:\n",
    " * each occurrence of state $s_t$ and the policy-recommended action $a_t$ in $N_{s_t, a_t}$ (table of frequencies for state-action pairs, initially zero)\n",
    " * each occurrence of $s_{t+1}$ resulting from $a_t$ on $s_t$ in $N_{s_{t+1}|s_t, a_t}$. (table of outcome frequencies given state-action pairs, initially zero)\n",
    "     \n",
    " It can thus estimate $P(s_{t+1}|s_t,a_t)$ as $N_{s_{t+1}|s_t, a_t}/N_{s_t, a_t}$, which in the limit of infinite trials, will converge to the true value.\n",
    " Using the transition probabilities thus estimated, it can apply **Policy Evaluation** to estimate the values $V(s)$ using properties of convergence of the Bellman functions.\n",
    "\n",
    "3. **Temporal-difference learning (TD)**\n",
    " \n",
    " Instead of explicitly building the transition model $P$, the temporal-difference model makes use of the expected closeness between the values of two consecutive states $s_{t}$ and $s_{t+1}$.\n",
    " For the transition $s_t$ to $s_{t+1}$, the update is written as:\n",
    " \n",
    "$$V^{\\pi}(s) \\leftarrow V^{\\pi}(s) + \\alpha \\left( R(s) + \\gamma V^{\\pi}(s') - V^{\\pi}(s) \\right)$$\n",
    " This model implicitly incorporates the transition probabilities by being weighed for each state by the number of times it is achieved from the current state. Thus, over a number of iterations, it converges similarly to the Bellman equations.\n",
    " The advantage of the TD learning model is its relatively simple computation at each step, rather than having to keep track of various counts.\n",
    " For $n_s$ states and $n_a$ actions the ADP model would have $n_s \\times n_a$ numbers $N_{s_t, a_t}$ and $n_s^2 \\times n_a$ numbers $N_{s_{t+1}|s_t, a_t}$ to keep track of. The TD model must only keep track of a value $V(s)$ for each state.\n",
    " \n",
    "## Example\n",
    "\n",
    "Lets create a new MDP class to represent a grid world MDP. Here assuming that the environment is fully observable, so that the agent always knows where it is. The rewards are **+1** and **-1** in the terminal states, and **-0.04** in the rest (to help learn the shortest route).\n",
    "\n",
    "![grid_mdp.jpg](attachment:grid_mdp.jpg)\n",
    "[[5](https://github.com/aimacode/aima-python/blob/master/mdp.ipynb)]\n",
    "\n",
    "> This is the environment for our agent.\n",
    "\n",
    "We also assume that the transitions are **Markovian**, that is, the probability of reaching state $s_{t+1}$ from state $s_t$ depends only on $s_t$ and not on the history of earlier states.\n",
    "Almost all stochastic decision problems can be reframed as a Markov Decision Process just by tweaking the definition of a state for that particular problem.\n",
    "\n",
    "However, the actions of our agent in this environment are unreliable. In other words, the motion of our agent is stochastic. \n",
    "\n",
    "More specifically, the agent may:\n",
    "* move correctly in the intended direction with a probability of $0.8$  \n",
    "* move $90^\\circ$ to the right of the intended direction with a probability $0.1$\n",
    "* move $90^\\circ$ to the left of the intended direction with a probability $0.1$\n",
    "\n",
    "The agent stays put if it bumps into a wall.\n",
    "\n",
    "![grid_mdp_agent.jpg](attachment:grid_mdp_agent.jpg)\n",
    "[[6](https://github.com/aimacode/aima-python/blob/master/mdp.ipynb)]\n",
    "\n",
    "For any given state, the actions the agent can take are encoded as given below:\n",
    "- Move Up: (0, 1)\n",
    "- Move Down: (0, -1)\n",
    "- Move Left: (-1, 0)\n",
    "- Move Right: (1, 0)\n",
    "- Do nothing: `None`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "orientations = EAST, NORTH, WEST, SOUTH = [(1, 0), (0, 1), (-1, 0), (0, -1)]\n",
    "turns = LEFT, RIGHT = (+1, -1)\n",
    "\n",
    "\n",
    "def turn_heading(heading, inc, headings=orientations):\n",
    "    return headings[(headings.index(heading) + inc) % len(headings)]\n",
    "\n",
    "\n",
    "def turn_right(heading):\n",
    "    return turn_heading(heading, RIGHT)\n",
    "\n",
    "\n",
    "def turn_left(heading):\n",
    "    return turn_heading(heading, LEFT)\n",
    "\n",
    "def vector_add(a, b):\n",
    "    \"\"\"Component-wise addition of two vectors.\"\"\"\n",
    "    return tuple(map(operator.add, a, b))\n",
    "\n",
    "class GridMDP(BaseMDP):\n",
    "    \"\"\"A two-dimensional grid MDP. All you have to do is\n",
    "    specify the grid as a list of lists of rewards; use None for an obstacle\n",
    "    (unreachable state). Also, you should specify the terminal states.\n",
    "    An action is an (x, y) unit vector; e.g. (1, 0) means move east.\"\"\"\n",
    "\n",
    "    def __init__(self, grid, terminals, init=(0, 0), gamma=.9):\n",
    "        grid.reverse()  # because we want row 0 on bottom, not on top\n",
    "        reward = {}\n",
    "        states = set()\n",
    "        self.rows = len(grid)\n",
    "        self.cols = len(grid[0])\n",
    "        self.grid = grid\n",
    "        for x in range(self.cols):\n",
    "            for y in range(self.rows):\n",
    "                if grid[y][x]:\n",
    "                    states.add((x, y))\n",
    "                    reward[(x, y)] = grid[y][x]\n",
    "        self.states = states\n",
    "        actlist = orientations\n",
    "        transitions = {}\n",
    "        for s in states:\n",
    "            transitions[s] = {}\n",
    "            for a in actlist:\n",
    "                transitions[s][a] = self.calculate_T(s, a)\n",
    "        BaseMDP.__init__(self, init, actlist=actlist,\n",
    "                     terminals=terminals, transitions=transitions,\n",
    "                     reward=reward, states=states, gamma=gamma)\n",
    "\n",
    "    def calculate_T(self, state, action):\n",
    "        if action:\n",
    "            return [(0.8, self.go(state, action)),\n",
    "                    (0.1, self.go(state, turn_right(action))),\n",
    "                    (0.1, self.go(state, turn_left(action)))]\n",
    "        else:\n",
    "            return [(0.0, state)]\n",
    "\n",
    "    def T(self, state, action):\n",
    "        return self.transitions[state][action] if action else [(0.0, state)]\n",
    "\n",
    "    def go(self, state, direction):\n",
    "        \"\"\"Return the state that results from going in this direction.\"\"\"\n",
    "\n",
    "        state1 = vector_add(state, direction)\n",
    "        return state1 if state1 in self.states else state\n",
    "\n",
    "    def to_grid(self, mapping):\n",
    "        \"\"\"Convert a mapping from (x, y) to v into a [[..., v, ...]] grid.\"\"\"\n",
    "\n",
    "        return list(reversed([[mapping.get((x, y), None)\n",
    "                               for x in range(self.cols)]\n",
    "                              for y in range(self.rows)]))\n",
    "\n",
    "    def to_arrows(self, policy):\n",
    "        chars = {(1, 0): '>', (0, 1): '^', (-1, 0): '<', (0, -1): 'v', None: '.'}\n",
    "        return self.to_grid({s: chars[a] for (s, a) in policy.items()})\n",
    "\n",
    "sequential_decision_environment = GridMDP([[-0.04, -0.04, -0.04, +1],\n",
    "                                           [-0.04, None, -0.04, -1],\n",
    "                                           [-0.04, -0.04, -0.04, -0.04]],\n",
    "                                          terminals=[(3, 2), (3, 1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Direct Value Estimation (DVE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculated Values are:\n",
      "\n",
      "(0, 1):0.8223110453090214\n",
      "(1, 2):0.9145057678057944\n",
      "(3, 2):1.0\n",
      "(0, 0):0.7679737120225807\n",
      "(2, 2):0.9574995732260843\n",
      "(1, 0):0.6952294921875\n",
      "(0, 2):0.874002043431046\n",
      "(2, 1):-0.06218255360921221\n",
      "(3, 1):-1.0\n"
     ]
    }
   ],
   "source": [
    "def run_single_trial(agent_program, mdp):\n",
    "    \"\"\"Execute trial for given agent_program\n",
    "    and mdp. mdp should be an instance of subclass\n",
    "    of mdp.MDP \"\"\"\n",
    "\n",
    "    def take_single_action(mdp, s, a):\n",
    "        \"\"\"\n",
    "        Select outcome of taking action a\n",
    "        in state s. Weighted Sampling.\n",
    "        \"\"\"\n",
    "        x = random.uniform(0, 1)\n",
    "        cumulative_probability = 0.0\n",
    "        for probability_state in mdp.T(s, a):\n",
    "            probability, state = probability_state\n",
    "            cumulative_probability += probability\n",
    "            if x < cumulative_probability:\n",
    "                break\n",
    "        return state\n",
    "\n",
    "    current_state = mdp.init\n",
    "    while True:\n",
    "        current_reward = mdp.R(current_state)\n",
    "        percept = (current_state, current_reward)\n",
    "        next_action = agent_program(percept)\n",
    "        if next_action is None:\n",
    "            break\n",
    "        current_state = take_single_action(mdp, current_state, next_action)\n",
    "\n",
    "class PassiveDVEAgent:\n",
    "    \"\"\"\n",
    "    Passive (non-learning) agent that uses direct value estimation\n",
    "    on a given MDP and policy.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, pi, mdp):\n",
    "        self.pi = pi\n",
    "        self.mdp = mdp\n",
    "        self.V = {}\n",
    "        self.s = None\n",
    "        self.a = None\n",
    "        self.s_history = []\n",
    "        self.r_history = []\n",
    "        self.init = mdp.init\n",
    "\n",
    "    def __call__(self, percept):\n",
    "        s1, r1 = percept\n",
    "        self.s_history.append(s1)\n",
    "        self.r_history.append(r1)\n",
    "        if s1 in self.mdp.terminals:\n",
    "            self.s = self.a = None\n",
    "        else:\n",
    "            self.s, self.a = s1, self.pi[s1]\n",
    "        return self.a\n",
    "\n",
    "    def estimate_V(self):\n",
    "        # this function can be called only if the MDP has reached a terminal state\n",
    "        # it will also reset the mdp history\n",
    "        assert self.a is None, 'MDP is not in terminal state'\n",
    "        assert len(self.s_history) == len(self.r_history)\n",
    "        # calculating the utilities based on the current iteration\n",
    "        V2 = {s: [] for s in set(self.s_history)}\n",
    "        for i in range(len(self.s_history)):\n",
    "            s = self.s_history[i]\n",
    "            V2[s] += [sum(self.r_history[i:])]\n",
    "        V2 = {k: sum(v) / max(len(v), 1) for k, v in V2.items()}\n",
    "        # resetting history\n",
    "        self.s_history, self.r_history = [], []\n",
    "        # setting the new utilities to the average of the previous \n",
    "        # iteration and this one\n",
    "        for k in V2.keys():\n",
    "            if k in self.V.keys():\n",
    "                self.V[k] = (self.V[k] + V2[k]) / 2\n",
    "            else:\n",
    "                self.V[k] = V2[k]\n",
    "        return self.V\n",
    "\n",
    "    def update_state(self, percept):\n",
    "        \"\"\"To be overridden in most cases. The default case\n",
    "        assumes the percept to be of type (state, reward)\"\"\"\n",
    "        return percept\n",
    "\n",
    "# We need instantiate the DVE with a policy\n",
    "policy = {\n",
    "    (0, 2): EAST,  (1, 2): EAST,  (2, 2): EAST,   (3, 2): None,\n",
    "    (0, 1): NORTH,                (2, 1): NORTH,  (3, 1): None,\n",
    "    (0, 0): NORTH, (1, 0): WEST,  (2, 0): WEST,   (3, 0): WEST, \n",
    "}    \n",
    "\n",
    "DVEagent = PassiveDVEAgent(policy, sequential_decision_environment)\n",
    "for i in range(200):\n",
    "    run_single_trial(DVEagent, sequential_decision_environment)\n",
    "    DVEagent.estimate_V()\n",
    "    \n",
    "print('Calculated Values are:\\n')\n",
    "print('\\n'.join([str(k)+':'+str(v) for k, v in DVEagent.V.items()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adaptive Dynamic Programming Agent\n",
    "\n",
    "`PassiveADPAgent` uses state transition and occurrence counts to estimate $P$, and then $V$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Transition table is empty.\n",
      "\n",
      "Calculated Values are:\n",
      "\n",
      "(0, 0):0.2976135513656844\n",
      "(0, 1):0.4036610839314542\n",
      "(1, 2):0.6521588438729153\n",
      "(3, 2):1.0\n",
      "(3, 0):0.0\n",
      "(3, 1):-1.0\n",
      "(2, 1):0.5396460099257762\n",
      "(2, 0):0.0\n",
      "(2, 2):0.8006301473972047\n",
      "(1, 0):0.20515874335052375\n",
      "(0, 2):0.5175335514814762\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "class PassiveADPAgent:\n",
    "    \"\"\"\n",
    "    Passive (non-learning) agent that uses adaptive dynamic programming\n",
    "    on a given MDP and policy.\n",
    "    \"\"\"\n",
    "\n",
    "    class ModelMDP(BaseMDP):\n",
    "        \"\"\"Class for implementing modified Version of input MDP with\n",
    "        an editable transition model P and a custom function T.\"\"\"\n",
    "\n",
    "        def __init__(self, init, actlist, terminals, gamma, states):\n",
    "            super().__init__(init, actlist, terminals, states=states, gamma=gamma)\n",
    "            nested_dict = lambda: defaultdict(nested_dict)\n",
    "            self.P = nested_dict()\n",
    "\n",
    "        def T(self, s, a):\n",
    "            \"\"\"Return a list of tuples with probabilities for states\n",
    "            based on the learnt model P.\"\"\"\n",
    "            return [(prob, res) for (res, prob) in self.P[(s, a)].items()]\n",
    "\n",
    "    def __init__(self, pi, mdp):\n",
    "        self.pi = pi\n",
    "        self.mdp = PassiveADPAgent.ModelMDP(mdp.init, mdp.actlist,\n",
    "                                            mdp.terminals, mdp.gamma, mdp.states)\n",
    "        self.V = {}\n",
    "        self.Nsa = defaultdict(int)\n",
    "        self.Ns1_sa = defaultdict(int)\n",
    "        self.s = None\n",
    "        self.a = None\n",
    "        self.visited = set()  # keeping track of visited states\n",
    "\n",
    "    def __call__(self, percept):\n",
    "        s1, r1 = percept\n",
    "        mdp = self.mdp\n",
    "        R, P, terminals, pi = mdp.reward, mdp.P, mdp.terminals, self.pi\n",
    "        s, a, Nsa, Ns1_sa, V = self.s, self.a, self.Nsa, self.Ns1_sa, self.V\n",
    "\n",
    "        if s1 not in self.visited:  # Reward is only known for visited state.\n",
    "            V[s1] = R[s1] = r1\n",
    "            self.visited.add(s1)\n",
    "        if s is not None:\n",
    "            Nsa[(s, a)] += 1\n",
    "            Ns1_sa[(s1, s, a)] += 1\n",
    "            # for each t such that Ns′|sa [t, s, a] is nonzero\n",
    "            for t in [res for (res, state, act), freq in Ns1_sa.items()\n",
    "                      if (state, act) == (s, a) and freq != 0]:\n",
    "                P[(s, a)][t] = Ns1_sa[(t, s, a)] / Nsa[(s, a)]\n",
    "\n",
    "        self.V = policy_evaluation(pi, V, mdp)\n",
    "        self.Nsa, self.Ns1_sa = Nsa, Ns1_sa\n",
    "        if s1 in terminals:\n",
    "            self.s = self.a = None\n",
    "        else:\n",
    "            self.s, self.a = s1, self.pi[s1]\n",
    "        return self.a\n",
    "\n",
    "    def update_state(self, percept):\n",
    "        \"\"\"To be overridden in most cases. The default case\n",
    "        assumes the percept to be of type (state, reward).\"\"\"\n",
    "        return percept\n",
    "\n",
    "ADPagent = PassiveADPAgent(policy, sequential_decision_environment)\n",
    "for i in range(200):\n",
    "    run_single_trial(ADPagent, sequential_decision_environment)\n",
    "    \n",
    "print('\\nCalculated Values are:\\n')\n",
    "print('\\n'.join([str(k)+':'+str(v) for k, v in ADPagent.V.items()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Passive Temporal Difference Agent\n",
    "\n",
    "`PassiveTDAgent` uses temporal differences to learn value estimates. We learn the difference between the states and backup the values to previous states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculated Values are:\n",
      "\n",
      "(0, 1):0.3271371707951126\n",
      "(1, 2):0.5591257360823622\n",
      "(3, 2):1\n",
      "(0, 0):0.2932197194224822\n",
      "(3, 0):0.0\n",
      "(3, 1):-1\n",
      "(2, 1):0.3377577562939222\n",
      "(2, 0):0.0\n",
      "(2, 2):0.7359514729008128\n",
      "(1, 0):0.19716297821904394\n",
      "(0, 2):0.41234081001453976\n"
     ]
    }
   ],
   "source": [
    "class PassiveTDAgent:\n",
    "    \"\"\"\n",
    "    The abstract class for a Passive (non-learning) agent that uses\n",
    "    temporal differences to learn value estimates. Override update_state\n",
    "    method to convert percept to state and reward. The mdp being provided\n",
    "    should be an instance of a subclass of the MDP Class.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, pi, mdp, alpha=None):\n",
    "\n",
    "        self.pi = pi\n",
    "        self.V = {s: 0. for s in mdp.states}\n",
    "        self.Ns = {s: 0 for s in mdp.states}\n",
    "        self.s = None\n",
    "        self.a = None\n",
    "        self.r = None\n",
    "        self.gamma = mdp.gamma\n",
    "        self.terminals = mdp.terminals\n",
    "\n",
    "        if alpha:\n",
    "            self.alpha = alpha\n",
    "        else:\n",
    "            self.alpha = lambda n: 1 / (1 + n)\n",
    "\n",
    "    def __call__(self, percept):\n",
    "        s1, r1 = self.update_state(percept)\n",
    "        pi, V, Ns, s, r = self.pi, self.V, self.Ns, self.s, self.r\n",
    "        alpha, gamma, terminals = self.alpha, self.gamma, self.terminals\n",
    "        if not Ns[s1]:\n",
    "            V[s1] = r1\n",
    "        if s is not None:\n",
    "            Ns[s] += 1\n",
    "            V[s] += alpha(Ns[s]) * (r + gamma * V[s1] - V[s])\n",
    "        if s1 in terminals:\n",
    "            self.s = self.a = self.r = None\n",
    "        else:\n",
    "            self.s, self.a, self.r = s1, pi[s1], r1\n",
    "        return self.a\n",
    "\n",
    "    def update_state(self, percept):\n",
    "        \"\"\"To be overridden in most cases. The default case\n",
    "        assumes the percept to be of type (state, reward).\"\"\"\n",
    "        return percept\n",
    "    \n",
    "# Learning rate\n",
    "alpha = lambda n: 60./(59+n)\n",
    "\n",
    "TDagent = PassiveTDAgent(policy, sequential_decision_environment, alpha = alpha)\n",
    "for i in range(200):\n",
    "    run_single_trial(TDagent,sequential_decision_environment)\n",
    "    \n",
    "print('\\nCalculated Values are:\\n')\n",
    "print('\\n'.join([str(k)+':'+str(v) for k, v in TDagent.V.items()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Active Reinforcement Learning\n",
    "\n",
    "Unlike Passive Reinforcement Learning in Active Reinforcement Learning we are not bound by a policy $\\pi$ and we need to select our actions. In other words the agent needs to learn an optimal policy $\\pi^*$. The fundamental tradeoff the agent needs to face is that of **exploration** vs. **exploitation**.\n",
    "\n",
    "## Q-Learning Agent\n",
    "\n",
    "In Q-Learning the agent learns an action-value function $Q(s_t,a_t)$ which gives the value of taking a given action in a particular state. $Q$-values are related directly to values as:\n",
    "\n",
    "$$V(s) =  \\operatorname{max}_{a} Q(s, a)$$\n",
    "\n",
    "\n",
    "Q-Learning does not require a transition model and hence is a **model-free** method. As with values, we can derive the update equation from Bellman's equations, which is for TD (Temporal Difference) Q-Learning:\n",
    "\n",
    "$$ Q(s_t,a_t) \\leftarrow Q(s_t,a_t) + \\alpha(R(s_t) + \\gamma \\operatorname{max}_{a_{t+1}} Q(s_{t+1},a_{t+1}) - Q(s_t,a_t)) $$\n",
    "\n",
    "And is calculated whenever we take action $a_t$, in state $s_t$ leading to state $s_{t+1}$. When exploring happens Q-learning uses the best $Q$-value, hence it is called an **off-policy** algorithm as it does not follow a policy when exploring.\n",
    "\n",
    "The below `QLearningAgent` class implements an exploration function $f$ which returns fixed $R_+$ (an optomisitic estimate of the best possible reward attainable in any state) until the agent has visited state, action $N_e$ number of times. The method `QLearningAgent.actions_in_state` returns actions possible in given state. It is useful when applying max and argmax operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLearningAgent:\n",
    "    \"\"\"\n",
    "     An exploratory Q-learning agent. It avoids having to learn the transition\n",
    "     model because the Q-value of a state can be related directly to those of\n",
    "     its neighbors.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, mdp, Ne, Rplus, alpha=None):\n",
    "\n",
    "        self.gamma = mdp.gamma\n",
    "        self.terminals = mdp.terminals\n",
    "        self.all_act = mdp.actlist\n",
    "        self.Ne = Ne  # iteration limit in exploration function\n",
    "        self.Rplus = Rplus  # large value to assign before iteration limit\n",
    "        self.Q = defaultdict(float)\n",
    "        self.Nsa = defaultdict(float)\n",
    "        self.s = None\n",
    "        self.a = None\n",
    "        self.r = None\n",
    "\n",
    "        if alpha:\n",
    "            self.alpha = alpha\n",
    "        else:\n",
    "            self.alpha = lambda n: 1. / (1 + n)\n",
    "\n",
    "    def f(self, u, n):\n",
    "        \"\"\"Exploration function. Returns fixed Rplus until\n",
    "        agent has visited state, action a Ne number of times.\n",
    "        Same as ADP agent\"\"\"\n",
    "        if n < self.Ne:\n",
    "            return self.Rplus\n",
    "        else:\n",
    "            return u\n",
    "\n",
    "    def actions_in_state(self, state):\n",
    "        \"\"\"Return actions possible in given state.\n",
    "        Useful for max and argmax.\"\"\"\n",
    "        if state in self.terminals:\n",
    "            return [None]\n",
    "        else:\n",
    "            return self.all_act\n",
    "\n",
    "    def __call__(self, percept):\n",
    "        s1, r1 = self.update_state(percept)\n",
    "        Q, Nsa, s, a, r = self.Q, self.Nsa, self.s, self.a, self.r\n",
    "        alpha, gamma, terminals = self.alpha, self.gamma, self.terminals,\n",
    "        actions_in_state = self.actions_in_state\n",
    "\n",
    "        if s in terminals:\n",
    "            Q[s, None] = r1\n",
    "        if s is not None:\n",
    "            Nsa[s, a] += 1\n",
    "            Q[s, a] += alpha(Nsa[s, a]) * (r + gamma * max(Q[s1, a1]\n",
    "                                                           for a1 in actions_in_state(s1)) - Q[s, a])\n",
    "        if s in terminals:\n",
    "            self.s = self.a = self.r = None\n",
    "        else:\n",
    "            self.s, self.r = s1, r1\n",
    "            self.a = max(actions_in_state(s1), key=lambda a1: self.f(Q[s1, a1], Nsa[s1, a1]))\n",
    "        return self.a\n",
    "\n",
    "    def update_state(self, percept):\n",
    "        \"\"\"To be overridden in most cases. The default case\n",
    "        assumes the percept to be of type (state, reward).\"\"\"\n",
    "        return percept\n",
    "    \n",
    "q_agent = QLearningAgent(sequential_decision_environment, Ne=5, Rplus=2, \n",
    "                         alpha=lambda n: 60./(59+n))\n",
    "for i in range(200):\n",
    "    run_single_trial(q_agent,sequential_decision_environment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us see the Q Values. The keys are state-action pairs. Where different actions correspond according to:\n",
    "\n",
    "* north = (0, 1)\n",
    "* south = (0,-1)\n",
    "* west = (-1, 0)\n",
    "* east = (1, 0)\n",
    "* Do nothing = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(float,\n",
       "            {((0, 0), (1, 0)): -0.18907011024435563,\n",
       "             ((0, 0), (0, 1)): 0.016055715142303573,\n",
       "             ((0, 0), (-1, 0)): -0.19414582836715144,\n",
       "             ((0, 0), (0, -1)): -0.197362264362732,\n",
       "             ((1, 0), (1, 0)): -0.13799385740012343,\n",
       "             ((1, 0), (0, 1)): -0.1955045464165534,\n",
       "             ((1, 0), (-1, 0)): -0.19234368038524843,\n",
       "             ((1, 0), (0, -1)): -0.2039606865759629,\n",
       "             ((2, 0), (1, 0)): -0.18680269373594197,\n",
       "             ((2, 0), (0, 1)): -0.10227424445451055,\n",
       "             ((2, 0), (-1, 0)): -0.19209191880753967,\n",
       "             ((2, 0), (0, -1)): -0.19412326272670127,\n",
       "             ((3, 0), (1, 0)): -0.5138163544867891,\n",
       "             ((3, 0), (0, 1)): -0.9104342940243259,\n",
       "             ((3, 0), (-1, 0)): -0.799633788177492,\n",
       "             ((3, 0), (0, -1)): -0.28011830872786025,\n",
       "             ((3, 1), None): -0.5609399712951512,\n",
       "             ((0, 1), (1, 0)): -0.16860689881582092,\n",
       "             ((0, 1), (0, 1)): 0.03331943373550701,\n",
       "             ((0, 1), (-1, 0)): -0.15340385689815017,\n",
       "             ((0, 1), (0, -1)): -0.1555735366525523,\n",
       "             ((0, 2), (1, 0)): 0.07948743858498493,\n",
       "             ((0, 2), (0, 1)): -0.10635561770608827,\n",
       "             ((0, 2), (-1, 0)): -0.10293706293706295,\n",
       "             ((0, 2), (0, -1)): -0.12112719741045669,\n",
       "             ((1, 2), (1, 0)): 0.17489854352873557,\n",
       "             ((1, 2), (0, 1)): -0.04,\n",
       "             ((1, 2), (-1, 0)): -0.0759999433406361,\n",
       "             ((1, 2), (0, -1)): -0.0759999433406361,\n",
       "             ((2, 2), (1, 0)): 0.2947289538798112,\n",
       "             ((2, 2), (0, 1)): 0.0782019494899496,\n",
       "             ((2, 2), (-1, 0)): 0.05370117293264979,\n",
       "             ((2, 2), (0, -1)): -0.005197935584750485,\n",
       "             ((3, 2), None): 0.3963241973633407,\n",
       "             ((2, 1), (1, 0)): -0.9021402660633233,\n",
       "             ((2, 1), (0, 1)): -0.2807786612937162,\n",
       "             ((2, 1), (-1, 0)): -0.05810436751079326,\n",
       "             ((2, 1), (0, -1)): -0.6123214831756907})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_agent.Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Value $V$ of each state is related to $Q$ by:\n",
    "\n",
    "$$V(s) =  \\operatorname{arg max}_{a} Q(s, a)$$\n",
    "\n",
    "Let us convert the $Q$ Values above into $V$ estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function __main__.<lambda>()>,\n",
       "            {(0, 0): 0.016055715142303573,\n",
       "             (1, 0): -0.13799385740012343,\n",
       "             (2, 0): -0.10227424445451055,\n",
       "             (3, 0): -0.28011830872786025,\n",
       "             (3, 1): -0.5609399712951512,\n",
       "             (0, 1): 0.03331943373550701,\n",
       "             (0, 2): 0.07948743858498493,\n",
       "             (1, 2): 0.17489854352873557,\n",
       "             (2, 2): 0.2947289538798112,\n",
       "             (3, 2): 0.3963241973633407,\n",
       "             (2, 1): -0.05810436751079326})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V = defaultdict(lambda: -1000.) # Very Large Negative Value for Comparison see below.\n",
    "for state_action, value in q_agent.Q.items():\n",
    "    state, action = state_action\n",
    "    if V[state] < value:\n",
    "        V[state] = value\n",
    "V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalization in Reinforcement Learning\n",
    "\n",
    "Presently we have looked at value functions and Q-functions over small state spaces, however these become computationally infeasible to compute for large state spaces (i.e. 100,000 states +). However practically in the real world, we have many more states. Or in the problem of games, these have many states as well, such as in chess there are $10^{120}$ states. We realistically cannot visit all these states in order to learn to to act optimally.\n",
    "\n",
    "We solve this using **function approximation**, where we learn a function to represent the Q-function or value function instead of a sampled look up table. This has the added benefit that through this compression it allows the agent to generalise from states it has not yet visited. For example by examining only one in every $10^{12}$ states in the game backgammon, it is possible to learn a value function that allows a program to play as well as any human.\n",
    "\n",
    "Looking at Value estimation, with function approximation this becomes a **supervised learning** problem. Suppose we look at the above grid MDP example, we could model the value estimation as:\n",
    "\n",
    "$$ \\hat{V}_\\theta (x,y) = f(x,y) = \\theta_0 + \\theta_1 x + \\theta_2 y $$\n",
    "\n",
    "Given a collection of trials, we can obtain samples for $\\hat{V}_\\theta (x,y)$, hence forming a readily solved supervised learning problem (Here illustrated with a toy linear regression formulation, however this could be a neural network). However we can benefit from *online learning*, updating the parameters after each trial. If $v(s)_j$ is the observed total reward from state $s$ onward in the $j$th trial, we can use the following parameter update equation:\n",
    "\n",
    "$$ \\theta  \\leftarrow \\theta + \\alpha (v_j(s) - \\hat{V}_\\theta(s))\\frac{\\partial \\hat{V}_\\theta(s)}{\\partial \\theta}$$\n",
    "\n",
    "Also known as the **delta rule**. We can also apply these ideas to temporal difference learners as well, the new versions of TD:\n",
    "\n",
    "$$ \\theta \\leftarrow \\theta + \\alpha [R(s_t) + \\gamma \\hat{V}_\\theta(s_{t+1})-\\hat{V}_\\theta(s_t)]\\frac{\\partial \\hat{V}_\\theta(s_t)}{\\partial \\theta}$$\n",
    "\n",
    "and Q-learning:\n",
    "\n",
    "$$ \\theta \\leftarrow \\theta + \\alpha [R(s_t) + \\gamma \\operatorname{max}_{a_{t+1}} \\hat{Q}_\\theta(s_{t+1}, a_{t+1})-\\hat{Q}_\\theta(s_t, a_t)]\\frac{\\partial \\hat{Q}_\\theta(s_t, a_t)}{\\partial \\theta}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Policy search\n",
    "\n",
    "A simple idea and approach to reinforcement learning, where we keep modifying the policy till we converge to a performance that is no longer increasing. We can parametrise a policy $\\pi$ (which maps states to actions), for example by a collection of parametrised Q-functions, i.e.\n",
    "\n",
    "$$ \\pi(s) = \\operatorname{max}_a \\hat{Q}_\\theta(s, a) $$\n",
    "\n",
    "In this example we would learn a Q-function that is close to the optimal $Q^*$. However this results in finding an optimal policy (an action sequence through the states), however we will not learn the optimal $Q^*$, and our learned Q-function could be a scale off the actual $Q*$ (e.g. $\\hat{Q}_\\theta(s, a) = Q^*(s,a)/20$). One problem learning a policy is that a policy function is a *discontinuous* function on the input, making it difficult to differentiate. This can be solved by using a **stochastic policy** $\\pi_\\theta(s,a)$ (probability of selection action $a$ in state $s$). One common representation is using the softmax function:\n",
    "\n",
    "$$\\pi_\\theta(s_t,a_t) = \\frac{e^{\\hat{Q}_\\theta(s_t, a_t)}}{\\sum_{a_{t+1}}e^{\\hat{Q}_\\theta(s_t, a_{t+1})}} $$\n",
    "\n",
    "Which gives a *continuous* function, which is readily differentiable. To improve the policy we define a **policy value** $\\rho(\\theta)$, that is the expected reward we get when following $\\pi_\\theta$. We then can perform gradient descent as usual. However this naive approach is inefficient due to the stochastic nature of the policy. One solution is the *REINFORCE* algorithm, to compute the **policy value gradient**:\n",
    "\n",
    "$$ \\nabla \\rho(\\theta) \\approx \\frac{1}{N} \\sum^N_{j=1} \\frac{\\nabla_\\theta \\pi_\\theta(s, a_j))R_j(s)}{\\pi_\\theta(s,a_j)}$$\n",
    "\n",
    "Where we have $N$ trials in all and the action taken on the $j$th trial is $a_j$. Policy gradients in general, are capable of learning a general approximation to the policy, however they suffer from high variance so they require allot of samples, which poses a challenge of sample efficiency in practice. However they often converge to some local minima which is good compared to Q-learning approximation, for which there are less guarantees (As we approximate the Bellman equation with a complicated function approximator).\n",
    "\n",
    "\n",
    "# Action spaces\n",
    "\n",
    "So far we have only discussed **discrete action spaces** (finite number of actions available for the agent to take), however we can also have **continous action spcaes** (such as controlling a robot in euclidean space). This affects the policy structure, we commonly have two variations:\n",
    "* **categorical policies** for **discrete action spaces**\n",
    "* **diagonal gaussian policies** for **continous action spaces**\n",
    "\n",
    "For diagonal gaussian policies, is a stochastic policy defined by a diagonal Gaussian distribution ($\\mathcal{N}(\\mu, \\Sigma)$) with covariance matrix only having entries on the diagonal (represented as a vector). An action can be sampled from this distribution (usually some spherical gaussian noise is added as well)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "In summary the **agent** formulation decides the kind of information that must be learned. The main three agent formulations are:\n",
    "\n",
    "* **Model-based** : Uses a world model $P$ and a value function $V$\n",
    "* **Model-free** (Value based) : Uses a action-value $Q$-value function\n",
    "* **Reflex design** (Policy based) : Uses a policy $\\pi$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}