Welcome to this tutorial on using OpenAI Gym for Policy Gradient Methods! In this tutorial, we will explore how to use the OpenAI Gym library to implement and test policy gradient algorithms.<\/p>\n
Policy gradient methods are a popular approach in the field of reinforcement learning (RL) for solving sequential decision-making problems. These methods directly parametrize the policy function and update the parameters based on the gradients of expected cumulative rewards.<\/p>\n
OpenAI Gym is a widely used RL library that provides a set of environments for benchmarking and developing RL algorithms. It offers a simple and unified interface to various RL tasks, making it an ideal choice for learning and experimenting with policy gradient algorithms.<\/p>\n
Before we get started, make sure you have OpenAI Gym installed on your system. If you haven’t installed it yet, you can do so by running the following command:<\/p>\n
pip install gym\n<\/code><\/pre>\nAdditionally, you may need to install other dependencies based on the specific algorithm you want to implement. For example, if you want to use TensorFlow for deep learning, you can install it using the following command:<\/p>\n
pip install tensorflow\n<\/code><\/pre>\nBasic Usage of OpenAI Gym<\/h2>\n
Let’s begin by understanding the basic usage of OpenAI Gym. OpenAI Gym provides a wide range of environments, each representing a specific task or problem. These environments can be created using the gym.make()<\/code> function by passing the environment ID as the argument. For example, to create an instance of the CartPole-v1 environment, you can use the following code:<\/p>\nimport gym\n\nenv = gym.make('CartPole-v1')\n<\/code><\/pre>\nOnce you have created an environment instance, you can interact with it using the following methods:<\/p>\n
\nreset()<\/code>: Resets the environment and returns the initial observation.<\/li>\nstep(action)<\/code>: Takes an action as an argument and performs one timestep in the environment. It returns the next observation, reward, done flag, and additional info.<\/li>\nrender()<\/code>: Renders the current state of the environment.<\/li>\n<\/ul>\nHere is an example that demonstrates the basic usage of OpenAI Gym:<\/p>\n
import gym\n\nenv = gym.make('CartPole-v1')\nobservation = env.reset()\n\ndone = False\nwhile not done:\n env.render()\n action = env.action_space.sample()\n observation, reward, done, info = env.step(action)\n\nenv.close()\n<\/code><\/pre>\nIn this example, we first create an instance of the CartPole-v1 environment. We then reset the environment to get the initial observation. We enter a loop where we render the current state of the environment, take a random action using env.action_space.sample()<\/code>, and perform one timestep in the environment using env.step(action)<\/code>. We continue this loop until the episode is done, and then we close the environment.<\/p>\nImplementing a Policy Gradient Algorithm<\/h2>\n
Now that we understand the basic usage of OpenAI Gym, let’s implement the policy gradient algorithm. In this tutorial, we will use the REINFORCE algorithm as an example of a policy gradient method.<\/p>\n
The REINFORCE algorithm computes the policy gradient estimates based on the average of the gradients of the logarithm of the policy probability multiplied by the reward-to-go. It then updates the policy parameters in the direction of these gradients to maximize the expected return.<\/p>\n
Here are the steps we will follow to implement the REINFORCE algorithm using OpenAI Gym:<\/p>\n
\n- Define the policy network<\/li>\n
- Choose the optimizer<\/li>\n
- Collect the trajectories<\/li>\n
- Compute the policy gradient<\/li>\n
- Update the policy parameters<\/li>\n<\/ol>\n
Step 1: Define the Policy Network<\/h3>\n
The first step is to define the policy network. In this tutorial, we will use a simple feedforward neural network with one hidden layer.<\/p>\n
Let’s start by defining the network architecture using TensorFlow:<\/p>\n
import tensorflow as tf\n\nclass PolicyNetwork(tf.keras.Model):\n def __init__(self, input_dim, output_dim):\n super(PolicyNetwork, self).__init__()\n self.hidden_layer = tf.keras.layers.Dense(16, activation='relu', input_dim=input_dim)\n self.output_layer = tf.keras.layers.Dense(output_dim, activation='softmax')\n\n def call(self, inputs):\n hidden = self.hidden_layer(inputs)\n logits = self.output_layer(hidden)\n return logits\n<\/code><\/pre>\nIn this code, we define a PolicyNetwork<\/code> class that inherits from tf.keras.Model<\/code>. We define the network layers in the constructor and implement the forward pass in the call()<\/code> method.<\/p>\nThe hidden_layer<\/code> is a dense layer with 16 neurons and ReLU activation. The output_layer<\/code> is a dense layer with output_dim<\/code> neurons (which is the number of possible actions in the environment) and softmax activation to output action probabilities.<\/p>\nStep 2: Choose the Optimizer<\/h3>\n
The next step is to choose an optimizer for updating the policy parameters. In this tutorial, we will use the Adam optimizer, which is a popular choice for gradient-based optimization.<\/p>\n
Here is an example of how to choose the Adam optimizer:<\/p>\n
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n<\/code><\/pre>\nIn this code, we create an instance of the Adam optimizer with a learning rate of 0.01.<\/p>\n
Step 3: Collect the Trajectories<\/h3>\n
The third step is to collect the trajectories by interacting with the environment. We will collect a batch of trajectories by running multiple episodes in the environment.<\/p>\n
Here is an example of how to collect the trajectories:<\/p>\n
def collect_trajectories(env, policy_network, num_episodes):\n trajectories = []\n\n for episode in range(num_episodes):\n observations = []\n actions = []\n rewards = []\n\n observation = env.reset()\n\n done = False\n while not done:\n action_logits = policy_network(tf.expand_dims(observation, axis=0))\n action = tf.random.categorical(action_logits, num_samples=1)[0, 0]\n next_observation, reward, done, _ = env.step(action.numpy())\n\n observations.append(observation)\n actions.append(action)\n rewards.append(reward)\n\n observation = next_observation\n\n trajectories.append((observations, actions, rewards))\n\n return trajectories\n<\/code><\/pre>\nIn this code, we define a collect_trajectories()<\/code> function that takes the environment, policy network, and the number of episodes as arguments.<\/p>\nWe loop over the episodes and interact with the environment by taking actions based on the policy network. We use tf.random.categorical()<\/code> to sample an action from the action probabilities. We collect the observations, actions, and rewards at each timestep. Finally, we append the trajectory to the trajectories<\/code> list and return it.<\/p>\nStep 4: Compute the Policy Gradient<\/h3>\n
The fourth step is to compute the policy gradient based on the collected trajectories. We will compute the gradient of the logarithm of the policy probability multiplied by the reward-to-go.<\/p>\n
Here is an example of how to compute the policy gradient:<\/p>\n
def compute_policy_gradient(trajectories, gamma=1.0):\n policy_gradients = []\n\n for observations, actions, rewards in trajectories:\n discounted_rewards = []\n\n cumulative_reward = 0\n for t in range(len(rewards) - 1, -1, -1):\n cumulative_reward = rewards[t] + gamma * cumulative_reward\n discounted_rewards.append(cumulative_reward)\n\n discounted_rewards.reverse()\n\n action_mask = tf.one_hot(actions, depth=env.action_space.n)\n discounted_rewards = tf.convert_to_tensor(discounted_rewards, dtype=tf.float32)\n action_mask = tf.cast(action_mask, tf.float32)\n\n policy_gradients.append(-tf.reduce_sum(tf.math.log(action_mask) * discounted_rewards, axis=1))\n\n return tf.concat(policy_gradients, axis=0)\n<\/code><\/pre>\nIn this code, we define a compute_policy_gradient()<\/code> function that takes the trajectories and a discount factor gamma<\/code> as arguments.<\/p>\nFor each trajectory, we compute the discounted rewards at each timestep. We iterate over the rewards in reverse order and multiply each reward by the discount factor and add it to the cumulative reward. We append the cumulative rewards to the discounted_rewards<\/code> list and reverse it to match the order of the observations and actions.<\/p>\nWe then convert the discounted rewards and action mask to tensors of type tf.float32<\/code>. We compute the gradient of the logarithm of the action probabilities multiplied by the discounted rewards using element-wise multiplication and summation. Finally, we concatenate the gradients and return them.<\/p>\nStep 5: Update the Policy Parameters<\/h3>\n
The final step is to update the policy parameters based on the computed policy gradient. We will use the optimizer.apply_gradients()<\/code> function to compute and apply the gradients.<\/p>\nHere is an example of how to update the policy parameters:<\/p>\n
def update_policy_parameters(policy_network, optimizer, policy_gradients):\n variables = policy_network.trainable_variables\n gradients = tape.gradient(policy_gradients, variables)\n optimizer.apply_gradients(zip(gradients, variables))\n<\/code><\/pre>\nIn this code, we define an update_policy_parameters()<\/code> function that takes the policy network, optimizer, and policy gradients as arguments.<\/p>\nWe first obtain the trainable variables of the policy network. We then use tf.GradientTape()<\/code> to record the gradient computation. We compute the gradients of the policy parameters by calling tape.gradient()<\/code> with the policy gradients and variables as arguments. Finally, we apply the gradients to update the policy parameters using the apply_gradients()<\/code> function.<\/p>\nPutting It All Together<\/h2>\n
Now that we have implemented the main steps of the REINFORCE algorithm, let’s put it all together and run the algorithm on an environment.<\/p>\n
Here is an example of how to run the REINFORCE algorithm using OpenAI Gym:<\/p>\n
import gym\nimport tensorflow as tf\n\nenv = gym.make('CartPole-v1')\n\npolicy_network = PolicyNetwork(env.observation_space.shape[0], env.action_space.n)\noptimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n\nfor iteration in range(1000):\n trajectories = collect_trajectories(env, policy_network, num_episodes=10)\n policy_gradients = compute_policy_gradient(trajectories)\n update_policy_parameters(policy_network, optimizer, policy_gradients)\n\nenv.close()\n<\/code><\/pre>\nIn this code, we first create an instance of the CartPole-v1 environment. We then create an instance of the PolicyNetwork<\/code> and the Adam optimizer.<\/p>\n