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:
- Create a neural network for the Actor and Critic models.
- Initialize the Actor and Critic models with random weights.
- Create a Replay Buffer to store the experiences.
- Iterate through the environment to collect experiences and store them in the Replay Buffer.
- Sample experiences from the Replay Buffer and use them to update the Actor and Critic models.
- Use the Actor model to select the best action and update the environment.
- 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
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