{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "kzN-Q9Zv1He1" }, "source": [ "# DQN\n", "\n", "The goal of this exercise is to implement DQN and to apply it to the cartpole balancing problem. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "try:\n", " import google.colab\n", " IN_COLAB = True\n", "except:\n", " IN_COLAB = False\n", "\n", "if IN_COLAB:\n", " !pip install -U gymnasium pygame moviepy\n", " !pip install gymnasium[box2d]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZuVpP0LaxKM5", "outputId": "948030f3-cdfb-43a1-882a-6140df11639b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gym version: 0.26.3\n" ] } ], "source": [ "import numpy as np\n", "rng = np.random.default_rng()\n", "import matplotlib.pyplot as plt\n", "import os\n", "from IPython.display import clear_output\n", "from collections import deque\n", "\n", "import gymnasium as gym\n", "print(\"gym version:\", gym.__version__)\n", "\n", "import pygame\n", "from moviepy.editor import ImageSequenceClip, ipython_display\n", "\n", "import tensorflow as tf\n", "import logging\n", "tf.get_logger().setLevel(logging.ERROR)\n", "\n", "class GymRecorder(object):\n", " \"\"\"\n", " Simple wrapper over moviepy to generate a .gif with the frames of a gym environment.\n", " \n", " The environment must have the render_mode `rgb_array_list`.\n", " \"\"\"\n", " def __init__(self, env):\n", " self.env = env\n", " self._frames = []\n", "\n", " def record(self, frames):\n", " \"To be called at the end of an episode.\"\n", " for frame in frames:\n", " self._frames.append(np.array(frame))\n", "\n", " def make_video(self, filename):\n", " \"Generates the gif video.\"\n", " directory = os.path.dirname(os.path.abspath(filename))\n", " if not os.path.exists(directory):\n", " os.mkdir(directory)\n", " self.clip = ImageSequenceClip(list(self._frames), fps=self.env.metadata[\"render_fps\"])\n", " self.clip.write_gif(filename, fps=self.env.metadata[\"render_fps\"], loop=0)\n", " del self._frames\n", " self._frames = []\n", "\n", "def running_average(x, N):\n", " kernel = np.ones(N) / N\n", " return np.convolve(x, kernel, mode='same')" ] }, { "cell_type": "markdown", "metadata": { "id": "EPakRvKRoA79" }, "source": [ "## Cartpole balancing task\n", "\n", "We are going to use the Cartpole balancing problem, which can be loaded with:\n", "\n", "```python\n", "gym.make('CartPole-v0')\n", "```\n", "\n", "States have 4 continuous values (position and speed of the cart, angle and speed of the pole) and 2 discrete outputs (going left or right). The reward is +1 for each transition where the pole is still standing (angle of less than 30° with the vertical). \n", "\n", "In CartPole-v0, the episode ends when the pole fails or after 200 steps. In CartPole-v1, the maximum episode length is 500 steps, which is too long for us, so we stick to v0 here.\n", "\n", "The maximal (undiscounted) return is therefore 200. Can DQN learn this?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 438 }, "id": "zBkpg0MDoIxJ", "outputId": "58411c0e-4248-4e15-9f5e-b284aeea321e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Return: 19.0\n", "MoviePy - Building file videos/cartpole.gif with imageio.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create the environment\n", "env = gym.make('CartPole-v0', render_mode=\"rgb_array_list\")\n", "recorder = GymRecorder(env)\n", "\n", "# Sample the initial state\n", "state, info = env.reset()\n", "\n", "# One episode:\n", "done = False\n", "return_episode = 0\n", "while not done:\n", "\n", " # Select an action randomly\n", " action = env.action_space.sample()\n", " \n", " # Sample a single transition\n", " next_state, reward, terminal, truncated, info = env.step(action)\n", "\n", " # End of the episode\n", " done = terminal or truncated\n", "\n", " # Update undiscounted return\n", " return_episode += reward\n", " \n", " # Go in the next state\n", " state = next_state\n", "\n", "print(\"Return:\", return_episode)\n", "\n", "recorder.record(env.render())\n", "video = \"videos/cartpole.gif\"\n", "recorder.make_video(video)\n", "ipython_display(video)" ] }, { "cell_type": "markdown", "metadata": { "id": "_3CIDqP41Wvf" }, "source": [ "As the problem is quite simple (4 state variables, 2 actions), DQN can run on a single CPU. However, we advise that you run the notebook on a GPU in Colab to avoid emptying the battery of your laptop too fast or making it too warm as training takes quite a long time.\n", "\n", "We will stop from now on to display the cartpole on colab, as we want to go fast." ] }, { "cell_type": "markdown", "metadata": { "id": "8hEvKXD1LDCq" }, "source": [ "## Creating the model\n", "\n", "The first step is to create the value network using `keras`. We will not need anything fancy: a simple fully connected network with 4 input neurons, two hidden layers of 64 neurons each and 2 output neurons will do the trick. ReLU activation functions all along and the Adam optimizer.\n", "\n", "**Q:** Which loss function should we use? Think about which arguments have to passed to `model.compile()` and what activation function is required in the output layer.\n", "\n", "We will need to create two identical networks: the trained network and the target network. You should therefore create a method that returns a compiled model, so it can be called two times. You should pass it the environment (so the network can know how many input and output neurons it needs) and the learning rate for the Adam optimizer.\n", "\n", "```python\n", "def create_model(env, lr):\n", " \n", " model = Sequential()\n", "\n", " # ...\n", "\n", " return model\n", "```\n", "\n", "**Q:** Implement the method accordingly." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "h67ZdBDZ6PKL" }, "outputs": [], "source": [ "def create_model(env, lr):\n", " \n", " model = tf.keras.models.Sequential()\n", " \n", " model.add(tf.keras.layers.Input(env.observation_space.shape))\n", " model.add(tf.keras.layers.Dense(64, activation='relu'))\n", " model.add(tf.keras.layers.Dense(64, activation='relu'))\n", " model.add(tf.keras.layers.Dense(env.action_space.n, activation='linear'))\n", " \n", " model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(learning_rate=lr))\n", " \n", " print(model.summary())\n", "\n", " return model" ] }, { "cell_type": "markdown", "metadata": { "id": "UF4OBQRtOpZZ" }, "source": [ "Let's test this method by creating the trained and target networks.\n", "\n", "**Important:** every time you call `create_model`, a new neural network will be instantiated but the previous ones will not be deleted. During this exercise, you may have to create hundreds of networks because of the incremental implementation of DQN: all networks will stay instantiated in the RAM, and your computer/colab tab will freeze after a while. Before creating new networks, delete all existing ones with:\n", "\n", "```python\n", "tf.keras.backend.clear_session()\n", "```\n", "\n", "**Q:** Create the trained and target networks. The learning rate does not matter for now. Instantiate the Cartpole environment and print the output of both networks for the initial state (`state, info = env.reset()`). Are they the same?\n", "\n", "*Hint:* `model.predict(X, verbose=0)` expects an array X of shape (N, 4), with N the number of examples. Here, we have only one example, so make sure to reshape `state` so it has the shape (1, 4) (otherwise tf will complain)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IywqFrVTOq8N", "outputId": "5298b903-b14b-4136-d8db-16c74b321199" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "State: [[0.01271655 0.00693787 0.00868186 0.00911883]]\n", "Metal device set to: Apple M1 Pro\n", "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense (Dense) (None, 64) 320 \n", " \n", " dense_1 (Dense) (None, 64) 4160 \n", " \n", " dense_2 (Dense) (None, 2) 130 \n", " \n", "=================================================================\n", "Total params: 4,610\n", "Trainable params: 4,610\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n", "Model: \"sequential_1\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_3 (Dense) (None, 64) 320 \n", " \n", " dense_4 (Dense) (None, 64) 4160 \n", " \n", " dense_5 (Dense) (None, 2) 130 \n", " \n", "=================================================================\n", "Total params: 4,610\n", "Trainable params: 4,610\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-11-25 11:15:13.944109: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", "2022-11-25 11:15:13.944323: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n", "2022-11-25 11:15:14.117562: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n", "2022-11-25 11:15:14.148074: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "----------\n", "State: [[0.01271655 0.00693787 0.00868186 0.00911883]]\n", "Prediction for the trained network: [0.00114096 0.00075357]\n", "Prediciton for the target network: [-0.00087994 0.00059144]\n", "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-11-25 11:15:14.260500: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n" ] } ], "source": [ "env = gym.make('CartPole-v0')\n", "\n", "state, info = env.reset()\n", "state = state.reshape((1, env.observation_space.shape[0]))\n", "print(\"State:\", state)\n", "\n", "tf.keras.backend.clear_session()\n", "trained_model = create_model(env, 0.001)\n", "target_model = create_model(env, 0.001)\n", "\n", "trained_prediction = trained_model.predict(state, verbose=0)[0]\n", "target_prediction = target_model.predict(state, verbose=0)[0]\n", "\n", "print('-'*10)\n", "print(\"State:\", state)\n", "print(\"Prediction for the trained network:\", trained_prediction)\n", "print(\"Prediciton for the target network:\", target_prediction)\n", "print('-'*10)" ] }, { "cell_type": "markdown", "metadata": { "id": "lJ5sZzqfQ2MK" }, "source": [ "The target network has the same structure as the trained network, but not the same weights, as they are randomly initialized. We want the target network $\\theta'$ to have exactly the same weights as the trained weights $\\theta$. You can obtain the weights of a network with:\n", "\n", "```python\n", "w = model.get_weights()\n", "```\n", "\n", "and set weights using:\n", "\n", "```python\n", "model.set_weights(w)\n", "```\n", "\n", "**Q:** Transfer the weights of the trained model to the target model. Compare their predictions for the current state." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VI9b0cmZQ2mr", "outputId": "48ea740d-9abd-4d88-942c-f895c27baa25" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------\n", "State: [[0.01271655 0.00693787 0.00868186 0.00911883]]\n", "Prediction for the trained network: [0.00114096 0.00075357]\n", "Prediciton for the target network: [0.00114096 0.00075357]\n", "----------\n" ] } ], "source": [ "target_model.set_weights(trained_model.get_weights())\n", "\n", "trained_prediction = trained_model.predict(state, verbose=0)[0]\n", "target_prediction = target_model.predict(state, verbose=0)[0]\n", "\n", "print('-'*10)\n", "print(\"State:\", state)\n", "print(\"Prediction for the trained network:\", trained_prediction)\n", "print(\"Prediciton for the target network:\", target_prediction)\n", "print('-'*10)" ] }, { "cell_type": "markdown", "metadata": { "id": "FUEWBYwUOpxm" }, "source": [ "## Experience replay memory\n", "\n", "The second thing that we need is the experience replay memory (or replay buffer). We need a container like a python list where we append (s, a, r, s', done) transitions (as in Q-learning), but with a maximal capacity: when there are already $C$ transitions in the list, one should stop appending to the list, but rather start writing at the beginning of the list.\n", "\n", "This would not be very hard to write, but it would take a lot of time and the risk is high to have hard-to-notice bugs. \n", "\n", "Here is a basic implementation of the replay buffer using **double-ended queues** (deque). A deque is list with a maximum capacity. If the deque is full, it starts writing again at the beginnning. Exactly what we need. This implementation uses one deque per element in (s, a, r, s', done), but one could also append the whole transition to a single deque.\n", "\n", "**Q:** Read the code of the ReplayBuffer and understand what it does." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "hf-CRYjS6PKO" }, "outputs": [], "source": [ "class ReplayBuffer:\n", " \"Basic implementation of the experience replay memory using separated deques.\"\n", " def __init__(self, max_capacity):\n", " self.max_capacity = max_capacity\n", " \n", " # deques for each element\n", " self.states = deque(maxlen=max_capacity)\n", " self.actions = deque(maxlen=max_capacity)\n", " self.rewards = deque(maxlen=max_capacity)\n", " self.next_states = deque(maxlen=max_capacity)\n", " self.dones = deque(maxlen=max_capacity)\n", " \n", " def append(self, state, action, reward, next_state, done):\n", " # Store data\n", " self.states.append(state)\n", " self.actions.append(action)\n", " self.rewards.append(reward)\n", " self.next_states.append(next_state)\n", " self.dones.append(done)\n", " \n", " def sample(self, batch_size):\n", " # Do not return samples if we do not have at least 2*batch_size transitions\n", " if len(self.states) < 2*batch_size: \n", " return []\n", " \n", " # Randomly choose the indices of the samples.\n", " indices = sorted(np.random.choice(np.arange(len(self.states)), batch_size, replace=False))\n", "\n", " # Return the corresponding\n", " return [np.array([self.states[i] for i in indices]), \n", " np.array([self.actions[i] for i in indices]), \n", " np.array([self.rewards[i] for i in indices]), \n", " np.array([self.next_states[i] for i in indices]), \n", " np.array([self.dones[i] for i in indices])]" ] }, { "cell_type": "markdown", "metadata": { "id": "CjmNxom5eftK" }, "source": [ "**Q:** Run a random agent on Cartpole (without rendering) for a few episodes and append each transition to a replay buffer with small capacity (e.g. 100). Sample a batch to check that everything makes sense." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ajn1ht1dco5N", "outputId": "d95112be-68f0-4681-e354-72625f656b75" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "States: [[-7.1889226e-05 -1.9597625e-02 1.1202861e-02 1.0221607e-02]\n", " [-5.1598279e-03 -2.1528986e-01 1.7882740e-02 3.1551802e-01]\n", " [-7.8443229e-02 -8.0612069e-01 1.3144340e-01 1.3234164e+00]\n", " [-4.6816234e-02 2.0717475e-01 4.1258741e-02 -2.8127652e-01]\n", " [-4.2442955e-02 -1.8412507e-01 3.6115777e-02 3.2783771e-01]\n", " [-9.1748878e-02 -3.8812301e-01 1.3098954e-01 8.2237107e-01]\n", " [ 7.3674910e-02 6.0291737e-01 -7.6641589e-02 -8.1436384e-01]\n", " [ 8.5733257e-02 7.9900050e-01 -9.2928864e-02 -1.1301358e+00]\n", " [ 1.2985145e-01 6.0852367e-01 -1.5678419e-01 -9.4639248e-01]\n", " [ 1.5025000e-02 7.9516947e-01 -4.6248329e-03 -1.1131814e+00]]\n", "Actions: [0 0 1 0 0 0 1 0 0 1]\n", "Rewards: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", "Next states: [[-4.6384174e-04 -2.1487844e-01 1.1407293e-02 3.0641800e-01]\n", " [-9.4656255e-03 -4.1066191e-01 2.4193101e-02 6.1378646e-01]\n", " [-9.4565637e-02 -6.1288112e-01 1.5791173e-01 1.0745906e+00]\n", " [-4.2672738e-02 1.1489304e-02 3.5633210e-02 2.4128472e-02]\n", " [-4.6125453e-02 -3.7974206e-01 4.2672534e-02 6.3168758e-01]\n", " [-9.9511340e-02 -5.8477044e-01 1.4743695e-01 1.1532161e+00]\n", " [ 8.5733257e-02 7.9900050e-01 -9.2928864e-02 -1.1301358e+00]\n", " [ 1.0171327e-01 6.0521013e-01 -1.1553158e-01 -8.6798614e-01]\n", " [ 1.4202191e-01 4.1582093e-01 -1.7571205e-01 -7.0678967e-01]\n", " [ 3.0928390e-02 9.9035186e-01 -2.6888460e-02 -1.4073114e+00]]\n", "Dones: [False False False False False False False False False False]\n" ] } ], "source": [ "env = gym.make('CartPole-v0')\n", "\n", "buffer = ReplayBuffer(100)\n", "\n", "for episode in range(10):\n", " \n", " # Reset\n", " state, info = env.reset()\n", " done = False\n", " \n", " # Sample the episode\n", " while not done:\n", " \n", " # Select an action randomly\n", " action = env.action_space.sample()\n", " \n", " # Perform the action\n", " next_state, reward, terminal, truncated, info = env.step(action)\n", "\n", " # End of the episode\n", " done = terminal or truncated\n", " \n", " # Store the transition\n", " buffer.append(state, action, reward, next_state, done)\n", " \n", " # Go in the next state\n", " state = next_state\n", " \n", "# Sample a minibatch\n", "batch = buffer.sample(10)\n", "print(\"States:\", batch[0])\n", "print(\"Actions:\", batch[1])\n", "print(\"Rewards:\", batch[2])\n", "print(\"Next states:\", batch[3])\n", "print(\"Dones:\", batch[4])" ] }, { "cell_type": "markdown", "metadata": { "id": "u29Tw9o6fRcw" }, "source": [ "## DQN agent\n", "\n", "Here starts the fun part. There are a lot of things to do here, but you will now whether it works or not only when everything has been (correctly) implemented. So here is a lot of text to read carefully, and then you are on your own.\n", "\n", "Reminder from the lecture:\n", "\n", "* Initialize value network $Q_{\\theta}$ and target network $Q_{\\theta'}$.\n", "\n", "* Initialize experience replay memory $\\mathcal{D}$ of maximal size $N$.\n", "\n", "* for $t \\in [0, T_\\text{total}]$:\n", "\n", " * Select an action $a_t$ based on $Q_\\theta(s_t, a)$, observe $s_{t+1}$ and $r_{t+1}$.\n", "\n", " * Store $(s_t, a_t, r_{t+1}, s_{t+1})$ in the experience replay memory.\n", "\n", " * Every $T_\\text{train}$ steps:\n", "\n", " * Sample a minibatch $\\mathcal{D}_s$ randomly from $\\mathcal{D}$.\n", "\n", " * For each transition $(s_k, a_k, r_k, s'_k)$ in the minibatch:\n", "\n", " * Compute the target value $t_k = r_k + \\gamma \\, \\max_{a'} Q_{\\theta'}(s'_k, a')$ using the target network.\n", "\n", " * Update the value network $Q_{\\theta}$ on $\\mathcal{D}_s$ to minimize:\n", "\n", " $$\\mathcal{L}(\\theta) = \\mathbb{E}_{\\mathcal{D}_s}[(t_k - Q_\\theta(s_k, a_k))^2]$$\n", "\n", " * Every $T_\\text{target}$ steps:\n", "\n", " * Update target network: $\\theta' \\leftarrow \\theta$.\n", "\n", "Here is the skeleton of the `DQNAgent` class that you have to write:\n", "\n", "```python\n", "class DQNAgent:\n", " \n", " def __init__(self, env, create_model, some_parameters):\n", " \n", " self.env = env\n", " \n", " # TODO: copy the parameters\n", "\n", " # TODO: Create the trained and target networks, copy the weights.\n", "\n", " # TODO: Create an instance of the replay memory\n", " \n", " def act(self, state):\n", "\n", " # TODO: Select an action using epsilon-greedy on the output of the trained model\n", "\n", " return action\n", " \n", " def update(self, batch):\n", " \n", " # TODO: train the model using the batch of transitions\n", " \n", " return loss # mse on the batch\n", "\n", " def train(self, nb_episodes):\n", "\n", " returns = []\n", " losses = []\n", "\n", " # TODO: Train the network for the given number of episodes\n", "\n", " return returns, losses\n", "\n", " def test(self):\n", "\n", " # TODO: one episode with epsilon temporarily set to 0\n", "\n", " return nb_steps # Should be 200 after learning\n", "```\n", "\n", "With this structure, it will be very simple to actually train the DQN on Cartpole:\n", "\n", "```python\n", "# Create the environment\n", "env = gym.make('CartPole-v1')\n", "\n", "# Create the agent\n", "agent = DQNAgent(env, create_model, other_parameters)\n", "\n", "# Train the agent\n", "returns, losses = agent.train(nb_episodes)\n", "\n", "# Plot the returns\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(returns)\n", "plt.plot(running_mean(returns, 10))\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Returns\")\n", "\n", "# Plot the losses\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(losses)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Training loss\")\n", "\n", "plt.show()\n", "\n", "# Test the network\n", "nb_steps = agent.test()\n", "print(\"Number of steps:\", nb_steps)\n", "```\n", "\n", "So you \"just\" have to fill the holes.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "4xwVaGiAif8G" }, "source": [ "### 1 - `__init__()`: Initializing the agent\n", "\n", "In this method, you should first copy the value of the parameters as attributes: learning rate, epsilon, gamma and so on.\n", "\n", "Suggested values: gamma = 0.99, learning_rate = 0.001 \n", "\n", "The second thing to do is to create the trained and target networks (with the same weights) and save them as attributes (the other methods will use them). Do not forget to clear the keras session first, otherwise the RAM will be quickly filled.\n", "\n", "The third thing is to create an instance of the ERM. Use a buffer limit of 5000 transitions (should be passed as a parameter). \n", "\n", "Do not hesitate to add other stuff as you implementing the other methods (e.g. counters)." ] }, { "cell_type": "markdown", "metadata": { "id": "jULjJ-EqkBuU" }, "source": [ "### 2 - `act()`: action selection\n", "\n", "We will use a simple $\\epsilon$-greedy method for the action selection, as in the previous exercises. \n", "\n", "The only difference is that we have to use the trained model to get the greedy action, using `trained_model.predict(X, verbose=0)`. This will return the Q-value of the two actions left and right. Use `argmax()` to return the greedy action (with probability 1 - $\\epsilon$). `env.action_space.sample()` should be used for the exploration (do not use the Q-network in that case, it is slow!).\n", "\n", "$\\epsilon$ will be scheduled with an initial value of 1.0 and an exponential decay rate of 0.0005 after each action. It is always better to keep a little exploration, even if $\\epsilon$ has decayed to 0. Keep a minimal value of 0.05 for epsilon. \n", "\n", "**Q:** Once this has been implemented, run your very slow random agent for 100 episodes to check everything works correctly." ] }, { "cell_type": "markdown", "metadata": { "id": "spNME6LFtWFb" }, "source": [ "### 3 - `train()`: training loop\n", "\n", "This method will be very similar to the Q-learning agent that you implemented previously. Do not hesitate to copy and paste.\n", "\n", "Here is the parts of the DQN algorithm that should be implemented:\n", "\n", "* for $t \\in [0, T_\\text{total}]$:\n", "\n", " * Select an action $a_t$ based on $Q_\\theta(s_t, a)$, observe $s_{t+1}$ and $r_{t+1}$.\n", "\n", " * Store $(s_t, a_t, r_{t+1}, s_{t+1})$ in the experience replay memory.\n", "\n", " * Every $T_\\text{train}$ steps:\n", "\n", " * Sample a minibatch $\\mathcal{D}_s$ randomly from $\\mathcal{D}$.\n", "\n", " * Update the trained network using $\\mathcal{D}_s$.\n", "\n", " * Every $T_\\text{target}$ steps:\n", "\n", " * Update target network: $\\theta' \\leftarrow \\theta$.\n", "\n", "The main difference with Q-learning is that `update()` will be called only every `T_train = 4` steps: the number of updates to the trained network will be 4 times smaller that the number of steps made in the environment. Beware that if the ERM does not have enough transitions yet (less than the batch size), you should not call `update()`.\n", "\n", "Updating the target network (copying the weights of the trained network) should happen every 100 steps. Pass these parameters to the constructor of the agent. \n", "\n", "The batch size can be set to 32." ] }, { "cell_type": "markdown", "metadata": { "id": "nAsS8V4NwAua" }, "source": [ "### 4 - `update()`: training the value network\n", "\n", "Using the provided minibatch, one should implement the following part of the DQN algorithm:\n", "\n", "* For each transition $(s_k, a_k, r_k, s'_k)$ in the minibatch:\n", "\n", " * Compute the target value $t_k = r_k + \\gamma \\, \\max_{a'} Q_{\\theta'}(s'_k, a')$ using the target network.\n", "\n", "* Update the value network $Q_{\\theta}$ on $\\mathcal{D}_s$ to minimize:\n", "\n", " $$\\mathcal{L}(\\theta) = \\mathbb{E}_{\\mathcal{D}_s}[(t_k - Q_\\theta(s_k, a_k))^2]$$\n", "\n", "So we just need to define the targets for each transition in the minibatch, and call `model.fit()` on the trained network to minimize the mse between the current predictions $Q_\\theta(s_k, a_k)$ and the target.\n", "\n", "But we have a problem: the network has two outputs for the actions left and right, but we have only one target for the action that was executed. We cannot compute the mse between a vector with 2 elements and a single value... They must have the same size.\n", "\n", "As we want only the train the output neuron corresponding to the action $a_k$, we are going to:\n", "\n", "1. Use the trained network to predict the Q-value of both actions $[Q_\\theta(s_k, 0), Q_\\theta(s_k, 1)]$.\n", "2. Replace one of the values with the target, for example $[Q_\\theta(s_k, 0), t_k]$ if the second action was chosen.\n", "3. Minimize the mse between $[Q_\\theta(s_k, 0), Q_\\theta(s_k, 1)]$ and $[Q_\\theta(s_k, 0), t_k]$.\n", "\n", "That way, the first output neuron has a squared error of 0, so it won't learn anything. Only the second output neuron will have a non-zero mse and learn.\n", "\n", "There are more efficient ways to do this (using masks), but this will do the trick, the drawback being that we have to make a forward pass on the minibatch before calling `fit()`.\n", "\n", "The rest is pretty much the same as for your Q-learning agent. Do not forget that actions leading to a terminal state should only use the reward as a target, not the complete Bellman target $r + \\gamma \\max Q$.\n", "\n", "*Hint:* as we sample a minibatch of 32 transitions, it is faster to call:\n", "\n", "```python\n", "Q_values = np.array(training_model.predict_on_batch(states))\n", "```\n", "\n", "than:\n", "\n", "```python\n", "Q_values = training_model.predict(states)\n", "```\n", "\n", "for reasons internal to tensorflow. Note that with tf2, you need to cast the result to numpy arrays as eager mode is now the default.\n", "\n", "The method should return the training loss, which is contained in the `History` object returned by `model.fit()`. `model.fit()` should be called for one epoch only, a batch size of 32, and `verbose` set to 0. " ] }, { "cell_type": "markdown", "metadata": { "id": "ym9zpNaK-wxl" }, "source": [ "### 5 - `test()`\n", "\n", "This method should run one episode with epsilon set to 0, without learning. The number of steps should be returned (do not bother discounting with gamma, the goal is to be up for 200 steps). " ] }, { "cell_type": "markdown", "metadata": { "id": "gmEoNa1V_d7l" }, "source": [ "**Q:** Let's go! Run the agent for 150 episodes and observe how fast it manages to keep the pole up for 200 steps. \n", "\n", "Beware that running the same network twice can lead to very different results. In particular, policy collapse (the network was almost perfect, but suddenly crashes and becomes random) can happen. Just be patient. \n", "\n", "You can visualize a test trial using the `GymRecorder`: you just need to set the `env` attribute of your DQN agent to a new env with the render mode `rgb_array_list` and record the frames at the end." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "kkUo7qS26PKS" }, "outputs": [], "source": [ "class DQNAgent:\n", " \n", " def __init__(self, env, create_model, learning_rate, epsilon, epsilon_decay, gamma, batch_size, target_update_period, training_update_period, buffer_limit):\n", " self.env = env\n", "\n", " self.learning_rate = learning_rate\n", " self.epsilon = epsilon\n", " self.epsilon_decay = epsilon_decay\n", " self.gamma = gamma\n", " self.batch_size = batch_size\n", " self.target_update_period = target_update_period\n", " self.training_update_period = training_update_period\n", " \n", " # Create the Q-network and the target network\n", " tf.keras.backend.clear_session() # start by deleting all existing models to be gentle on the RAM\n", " self.model = create_model(self.env, self.learning_rate)\n", " self.target_model = create_model(self.env, self.learning_rate)\n", " self.target_model.set_weights(self.model.get_weights())\n", "\n", " # Create the replay memory\n", " self.buffer = ReplayBuffer(buffer_limit)\n", " \n", " def act(self, state):\n", "\n", " # epsilon-greedy\n", " if np.random.rand() < self.epsilon: # Random selection\n", " action = self.env.action_space.sample()\n", " else: # Use the Q-network to get the greedy action\n", " action = self.model.predict(state.reshape((1, env.observation_space.shape[0])), verbose=0)[0].argmax()\n", "\n", " # Decay epsilon\n", " self.epsilon *= 1 - self.epsilon_decay\n", " self.epsilon = max(0.05, self.epsilon)\n", "\n", " return action\n", " \n", " def update(self, batch):\n", " \n", " # Get the minibatch\n", " states, actions, rewards, next_states, dones = batch \n", " \n", " # Predict the Q-values in the current state\n", " targets = np.array(self.model.predict_on_batch(states))\n", " \n", " # Predict the Q-values in the next state using the target model\n", " next_Q_value = np.array(self.target_model.predict_on_batch(next_states)).max(axis=1)\n", " \n", " # Terminal states have a value of 0\n", " next_Q_value[dones] = 0.0\n", " \n", " # Compute the target\n", " for i in range(self.batch_size):\n", " targets[i, actions[i]] = rewards[i] + self.gamma * next_Q_value[i]\n", " \n", " # Train the model on the minibatch\n", " history = self.model.fit(states, targets, epochs=1, batch_size=self.batch_size, verbose=0)\n", " \n", " return history.history['loss'][0]\n", "\n", " def train(self, nb_episodes):\n", "\n", " steps = 0\n", " returns = []\n", " losses = []\n", "\n", " for episode in range(nb_episodes):\n", " \n", " # Reset\n", " state, info = self.env.reset()\n", " done = False\n", " steps_episode = 0\n", " return_episode = 0\n", "\n", " loss_episode = []\n", " \n", " # Sample the episode\n", " while not done:\n", "\n", " # Select an action \n", " action = self.act(state)\n", " \n", " # Perform the action\n", " next_state, reward, terminal, truncated, info = self.env.step(action)\n", "\n", " # End of the episode\n", " done = terminal or truncated\n", " \n", " # Store the transition\n", " self.buffer.append(state, action, reward, next_state, done)\n", " \n", " # Sample a minibatch\n", " batch = self.buffer.sample(self.batch_size)\n", " \n", " # Train the NN on the minibatch\n", " if len(batch) > 0 and steps % self.training_update_period == 0:\n", " loss = self.update(batch)\n", " loss_episode.append(loss)\n", "\n", " # Update the target model\n", " if steps > self.target_update_period and steps % self.target_update_period == 0:\n", " self.target_model.set_weights(self.model.get_weights())\n", " \n", " # Go in the next state\n", " state = next_state\n", " \n", " # Increment time\n", " steps += 1\n", " steps_episode += 1\n", " return_episode += reward\n", " \n", " if done:\n", " break\n", " \n", " # Store info\n", " returns.append(return_episode)\n", " losses.append(np.mean(loss_episode))\n", "\n", " # Print info\n", " clear_output(wait=True)\n", " print('Episode', episode+1)\n", " print(' total steps:', steps)\n", " print(' length of the episode:', steps_episode)\n", " print(' return of the episode:', return_episode)\n", " print(' current loss:', np.mean(loss_episode))\n", " print(' epsilon:', self.epsilon)\n", "\n", " return returns, losses\n", "\n", " def test(self, render=True):\n", "\n", " old_epsilon = self.epsilon\n", " self.epsilon = 0.0\n", " \n", " state, info = self.env.reset()\n", " nb_steps = 0\n", " done = False\n", " \n", " while not done:\n", " action = self.act(state)\n", " next_state, reward, terminal, truncated, info = self.env.step(action)\n", " done = terminal or truncated\n", " state = next_state\n", " nb_steps += 1\n", " \n", " self.epsilon = old_epsilon\n", " return nb_steps\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "-X9trhsf6PKV" }, "outputs": [], "source": [ "# Parameters\n", "nb_episodes = 150\n", "batch_size = 32\n", "\n", "epsilon = 1.0\n", "epsilon_decay = 0.0005\n", "\n", "gamma = 0.99\n", "\n", "learning_rate = 0.005 \n", "buffer_limit = 5000\n", "target_update_period = 100\n", "training_update_period = 4" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 865 }, "id": "das8K2JL6PKc", "outputId": "d920d07f-198c-4591-d6c3-2945fea5c287" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Episode 150\n", " total steps: 17021\n", " length of the episode: 138\n", " return of the episode: 138.0\n", " current loss: 7.081971720286778\n", " epsilon: 0.05\n" ] }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create the environment\n", "env = gym.make('CartPole-v0')\n", "\n", "# Create the agent\n", "agent = DQNAgent(env, create_model, learning_rate, epsilon, epsilon_decay, gamma, batch_size, target_update_period, training_update_period, buffer_limit)\n", "\n", "# Train the agent\n", "returns, losses = agent.train(nb_episodes)\n", "\n", "# Plot the returns\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(returns)\n", "plt.plot(running_average(returns, 10))\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Returns\")\n", "\n", "# Plot the losses\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(losses)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Training loss\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s429TELOs2tv", "outputId": "4fbd292b-0f83-4d33-fa86-b103d035f095" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of steps: 162\n", "MoviePy - Building file videos/cartpole-dqn.gif with imageio.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Test the network\n", "env = gym.make('CartPole-v0', render_mode=\"rgb_array_list\")\n", "recorder = GymRecorder(env)\n", "agent.env = env\n", "\n", "nb_steps = agent.test()\n", "recorder.record(env.render())\n", "print(\"Number of steps:\", nb_steps)\n", "\n", "video = \"videos/cartpole-dqn.gif\"\n", "recorder.make_video(video)\n", "ipython_display(video, loop=0, autoplay=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "iMhXV-ezD8um" }, "source": [ "**Q:** How does the loss evolve? Does it make sense?" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Tl8umyzSEEPG" }, "source": [ "**A:** The Q-values are non-stationary: the initial Q-values are very small (the agent fails almost immediately), while they are around 100 after training (200 steps with a reward of +1, but discounted with gamma). The mse increases with the magnitude of the Q-values, so the loss is a poor indicator of the convergence of the network. " ] }, { "cell_type": "markdown", "metadata": { "id": "K1m4JWFF_0Cs" }, "source": [ "## Reward scaling\n", "\n", "**Q:** Do a custom test trial after training (i.e. do not call test(), but copy and adapt its code) and plot the Q-value of the selected action at each time step. Do you think it is a good output for the network? Could it explain why learning is so slow?" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 388 }, "id": "MC3dqB8PBkjn", "outputId": "5c417c70-e4d7-4516-b8b7-227cb09c6a46" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "env = gym.make('CartPole-v0')\n", "agent.env = env\n", "\n", "# No exploration\n", "agent.epsilon = 0.0\n", " \n", "state, info = agent.env.reset()\n", "done = False\n", "\n", "Q_values = []\n", "\n", "while not done:\n", " action = agent.act(state)\n", " Q_values.append(agent.model.predict(state.reshape((1, 4)), verbose=0)[0][action])\n", " next_state, reward, terminal, truncated, info = agent.env.step(action)\n", " done = terminal or truncated\n", " state = next_state\n", "\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(Q_values)\n", "plt.xlabel(\"Steps\")\n", "plt.ylabel(\"Q-value\")\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "3ocDltRNDjab" }, "source": [ "**A:** The predicted Q-values at the beginning of learning are close to 0, as the weights are randomly initialized. They should grow to around 100, which takes a lot of time. If the target Q-values were around 1, learning might be much faster." ] }, { "cell_type": "markdown", "metadata": { "id": "RzxvN0OcCuGd" }, "source": [ "**Q:** Implement **reward scaling** by dividing the received rewards by a fixed factor of 100 when computing the Bellman targets. That way, the final Q-values will be around 1, what may be much easier to learned.\n", "\n", "*Tip:* in order to avoid a huge copy and paste, you can inherit from your DQNAgent and only reimplement the desired function:\n", "\n", "```python\n", "class ScaledDQNAgent (DQNAgent):\n", " def update(self, batch):\n", " # Change the content of this function only\n", "```\n", "\n", "You should reduce a bit the learning rate (e.g. 0.001) as the magnitude of the targets has changed. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "MQnS0eUkCukm" }, "outputs": [], "source": [ "class ScaledDQNAgent(DQNAgent):\n", " \n", " def update(self, batch):\n", " \n", " # Get the minibatch\n", " states, actions, rewards, next_states, dones = batch \n", " \n", " # Predict the Q-values in the current state\n", " targets = np.array(self.model.predict_on_batch(states))\n", " \n", " # Predict the Q-values in the next state using the target model\n", " next_Q_value = np.array(self.target_model.predict_on_batch(next_states)).max(axis=1)\n", " \n", " # Terminal states have a value of 0\n", " next_Q_value[dones] = 0.0\n", " \n", " # Compute the target\n", " for i in range(self.batch_size):\n", " targets[i, actions[i]] = rewards[i]/100. + self.gamma * next_Q_value[i]\n", " \n", " # Train the model on the minibatch\n", " history = self.model.fit(states, targets, epochs=1, batch_size=self.batch_size, verbose=0)\n", " \n", " return history.history['loss'][0]\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "EuRvm46QFLgM", "outputId": "2c4d228a-f36a-4f13-9ba6-c971ced0925b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Episode 150\n", " total steps: 18540\n", " length of the episode: 200\n", " return of the episode: 200.0\n", " current loss: 0.0003936138337303419\n", " epsilon: 0.05\n" ] }, { "data": { "text/plain": [ "Text(0, 0.5, 'Training loss')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create the environment\n", "env = gym.make('CartPole-v0')\n", "\n", "# Create the agent\n", "learning_rate = 0.002\n", "agent = ScaledDQNAgent(env, create_model, learning_rate, epsilon, epsilon_decay, gamma, batch_size, target_update_period, training_update_period, buffer_limit)\n", "\n", "# Train the agent\n", "returns, losses = agent.train(nb_episodes)\n", "\n", "# Plot the returns\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(returns)\n", "plt.plot(running_average(returns, 10))\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Returns\")\n", "\n", "# Plot the losses\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(losses)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Training loss\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Test the network\n", "env = gym.make('CartPole-v0', render_mode=\"rgb_array_list\")\n", "recorder = GymRecorder(env)\n", "agent.env = env\n", "\n", "agent.epsilon = 0.0\n", "\n", "# Q-values \n", "state, info = agent.env.reset()\n", "done = False\n", "Q_values = []\n", "\n", "while not done:\n", " action = agent.act(state)\n", " Q_values.append(agent.model.predict(state.reshape((1, 4)), verbose=0)[0][action])\n", " next_state, reward, terminal, truncated, info = agent.env.step(action)\n", " done = terminal or truncated\n", " state = next_state\n", "\n", "# Plot the Q-values\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(Q_values)\n", "plt.xlabel(\"Steps\")\n", "plt.ylabel(\"Q-value\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JNCsIgX931Ba", "outputId": "6a77b08d-7d46-47dd-9ff6-8dca35625747" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MoviePy - Building file videos/cartpole-dqn-scaled.gif with imageio.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "data": { "text/html": [ "