python-pytorchHow can I implement Deep Deterministic Policy Gradients (DDPG) using PyTorch?

Deep Deterministic Policy Gradients (DDPG) is an off-policy reinforcement learning algorithm which is used to solve continuous control problems. It can be implemented using PyTorch by following the steps below:

1. Create a neural network for the Actor and Critic models.
2. Initialize the Actor and Critic models with random weights.
3. Create a Replay Buffer to store the experiences.
4. Iterate through the environment to collect experiences and store them in the Replay Buffer.
5. Sample experiences from the Replay Buffer and use them to update the Actor and Critic models.
6. Use the Actor model to select the best action and update the environment.
7. Calculate the reward and use it to update the Critic model.

Example code

# Create Actor and Critic models
actor = ActorNetwork(state_dim, action_dim, max_action)
critic = CriticNetwork(state_dim, action_dim)

# Initialize Actor and Critic models with random weights
actor.apply(init_weights)
critic.apply(init_weights)

# Create Replay Buffer
replay_buffer = ReplayBuffer()

# Iterate through the environment
for episode in range(max_episodes):
    # Collect experiences
    replay_buffer.add(state, action, next_state, reward, done)

    # Sample experiences from Replay Buffer
    experiences = replay_buffer.sample(batch_size)

    # Update Actor and Critic models
    update_actor(actor, experiences)
    update_critic(critic, experiences)

    # Select best action
    action = actor(state)

    # Update environment
    next_state, reward, done = env.step(action)

    # Update Critic model
    update_critic(critic, state, action, reward, next_state, done)

Helpful links

• https://pytorch.org/tutorials/intermediate/ddpg_pendulum.html
• https://arxiv.org/pdf/1509.02971.pdf

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