{ "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" ] }, { "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: 0.28.1\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: 29.0\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: [[-0.04610259 -0.01609224 0.04184466 -0.04606003]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-01-18 10:30:13.899822: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1 Pro\n", "2024-01-18 10:30:13.899849: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n", "2024-01-18 10:30:13.899867: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n", "2024-01-18 10:30:13.899901: 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", "2024-01-18 10:30:13.899917: 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", "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "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: 4610 (18.01 KB)\n", "Trainable params: 4610 (18.01 KB)\n", "Non-trainable params: 0 (0.00 Byte)\n", "_________________________________________________________________\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "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: 4610 (18.01 KB)\n", "Trainable params: 4610 (18.01 KB)\n", "Non-trainable params: 0 (0.00 Byte)\n", "_________________________________________________________________\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-01-18 10:30:14.573949: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "None\n", "----------\n", "State: [[-0.04610259 -0.01609224 0.04184466 -0.04606003]]\n", "Prediction for the trained network: [ 0.02012723 -0.01722453]\n", "Prediction for the target network: [-0.00624603 0.01739823]\n", "----------\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(\"Prediction 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: [[-0.04610259 -0.01609224 0.04184466 -0.04606003]]\n", "Prediction for the trained network: [ 0.02012723 -0.01722453]\n", "Prediction for the target network: [ 0.02012723 -0.01722453]\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(\"Prediction 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: [[-5.0820481e-02 -1.9829975e-01 1.8521409e-01 7.0651567e-01]\n", " [-2.9969456e-02 1.3707675e-02 -3.1773280e-02 -1.7834282e-02]\n", " [-1.6467696e-02 4.0724629e-01 -5.4788038e-02 -6.7666495e-01]\n", " [ 8.8674217e-02 1.1545870e+00 -1.3338995e-01 -1.9483823e+00]\n", " [-5.4834139e-02 -5.8381045e-01 2.3132987e-02 9.0583807e-01]\n", " [ 7.2899368e-04 2.1862607e-01 1.4287900e-02 -2.5678131e-01]\n", " [ 5.1015150e-03 2.3303077e-02 9.1522746e-03 4.0373772e-02]\n", " [ 6.9507645e-03 -3.6736843e-01 1.2712554e-02 6.3518500e-01]\n", " [-3.7665710e-02 -5.6538355e-01 8.9652494e-02 9.9486345e-01]\n", " [-8.3056085e-02 -5.7150620e-01 1.7302851e-01 1.1416848e+00]]\n", "Actions: [0 1 0 0 0 0 1 0 1 1]\n", "Rewards: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", "Next states: [[-5.4786474e-02 -3.9543873e-01 1.9934440e-01 1.0513088e+00]\n", " [-2.9695302e-02 2.0927054e-01 -3.2129969e-02 -3.2037029e-01]\n", " [-8.3227698e-03 2.1292669e-01 -6.8321340e-02 -4.0172252e-01]\n", " [ 1.1176596e-01 9.6111327e-01 -1.7235760e-01 -1.6998502e+00]\n", " [-6.6510350e-02 -7.7923793e-01 4.1249748e-02 1.2057012e+00]\n", " [ 5.1015150e-03 2.3303077e-02 9.1522746e-03 4.0373772e-02]\n", " [ 5.5675767e-03 2.1829259e-01 9.9597499e-03 -2.4940753e-01]\n", " [-3.9660427e-04 -5.6266540e-01 2.5416255e-02 9.3184412e-01]\n", " [-4.8973382e-02 -3.7156767e-01 1.0954977e-01 7.3162979e-01]\n", " [-9.4486214e-02 -3.7901506e-01 1.9586220e-01 9.0787643e-01]]\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-v0')\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": 12, "metadata": { "id": "-X9trhsf6PKV" }, "outputs": [], "source": [ "# Exploration with scheduling\n", "epsilon = 1.0\n", "epsilon_decay = 0.0005\n", "\n", "# Discount\n", "gamma = 0.95\n", "\n", "# Number of episodes\n", "nb_episodes = 250\n", "\n", "# Batch size\n", "batch_size = 32\n", "\n", "# Learning rate\n", "learning_rate = 0.005 \n", "\n", "# Size of the replay buffer\n", "buffer_limit = 5000\n", "\n", "# Update of the replay buffer\n", "target_update_period = 100\n", "\n", "# Training period\n", "training_update_period = 4" ] }, { "cell_type": "code", "execution_count": 13, "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 250\n", " total steps: 4047\n", " length of the episode: 14\n", " return of the episode: 14.0\n", " current loss: 131.9358113606771\n", " epsilon: 0.13212510452461754\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(\n", " env, create_model, \n", " learning_rate, epsilon, epsilon_decay, gamma, \n", " batch_size, target_update_period, training_update_period, buffer_limit\n", ")\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: 9\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": 15, "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 85\n", " total steps: 1541\n", " length of the episode: 16\n", " return of the episode: 16.0\n", " current loss: 0.032923879101872444\n", " epsilon: 0.46269245519982455\n" ] } ], "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": null, "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": null, "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": [ "
" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recorder.record(env.render())\n", "\n", "video = \"videos/cartpole-dqn-scaled.gif\"\n", "recorder.make_video(video)\n", "ipython_display(video, loop=0, autoplay=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "uq75lUygCu2P" }, "source": [ "**Q:** Depending on the time left and your motivation, vary the different parameters to understand their influence: learning rate, target update frequency, training update frequency, epsilon decay, gamma, etc. Change the size of the network. If you find better hyperparameters than what is proposed, please report them for next year!" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "12-DQN-solution.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.7" }, "vscode": { "interpreter": { "hash": "932956c8e5d2f79d68ff59e849758b6e4ddbf01f7f22c7d8bb3532c38341d908" } } }, "nbformat": 4, "nbformat_minor": 4 }