Overview of DDPG Implementation

This guide will walk you through creating an intelligent agent using the Deep Deterministic Policy Gradient (DDPG) algorithm, an advanced method in deep reinforcement learning.

Understanding the Mountain Car Environment

Key Concepts in Reinforcement Learning

• Fundamentals of Reinforcement Learning
• Temporal Difference Learning
• Q-Learning
• Deep Q Learning Techniques
• Introduction to Actor-Critic Methods
• Deep Deterministic Policy Gradient (DDPG) Explained

DDPG is a model-free, off-policy algorithm that employs the Actor-Critic method and is inspired by Deep Q-Networks. Its architecture includes:

• Replay Buffer
• Actor-Critic Neural Network
• Exploration Noise
• Target Network
• Soft Target Updates

Role of the Replay Buffer

The Replay Buffer is essential for storing transitions and rewards ( (S_t, A_t, R_t, S_{t+1}) ) collected during the agent's interaction with the environment. This component enhances learning speed and stabilizes the DDPG process by:

• Reducing sample correlation through diverse experiences.
• Facilitating off-policy learning by sampling from past experiences.
• Maximizing sample efficiency.

class ReplayBuffer:

"""

Reference:

Accepts tuples of (state, next_state, action, reward, done)

"""

def __init__(self, max_size=capacity):

"""Initialize the Replay Buffer."""

self.storage = []

self.max_size = max_size

self.ptr = 0

def push(self, data):

if len(self.storage) == self.max_size:

self.storage[int(self.ptr)] = data

self.ptr = (self.ptr + 1) % self.max_size

else:

self.storage.append(data)

def sample(self, batch_size):

"""Sample a batch of experiences."""

ind = np.random.randint(0, len(self.storage), size=batch_size)

state, next_state, action, reward, done = [], [], [], [], []

for i in ind:

st, n_st, act, rew, dn = self.storage[i]

state.append(np.array(st, copy=False))

next_state.append(np.array(n_st, copy=False))

action.append(np.array(act, copy=False))

reward.append(np.array(rew, copy=False))

done.append(np.array(dn, copy=False))

return np.array(state), np.array(next_state), np.array(action), np.array(reward).reshape(-1, 1), np.array(done).reshape(-1, 1)

Actor-Critic Neural Network Structure

The Actor-Critic framework involves two neural networks: an Actor and a Critic.

Actor Model:

• Input: Environment state
• Output: Continuous action

Critic Model:

• Input: Both the environment state and the action
• Output: Q-value representing the expected total reward for the state-action pair.

class Actor(nn.Module):

"""

The Actor model processes state observations to output an action.

"""

def __init__(self, n_states, action_dim, hidden1):

super(Actor, self).__init__()

self.net = nn.Sequential(

nn.Linear(n_states, hidden1),

nn.ReLU(),

nn.Linear(hidden1, hidden1),

nn.ReLU(),

nn.Linear(hidden1, hidden1),

nn.ReLU(),

nn.Linear(hidden1, 1)

)

def forward(self, state):

return self.net(state)

class Critic(nn.Module):

"""

The Critic model estimates Q-values based on state-action pairs.

"""

def __init__(self, n_states, action_dim, hidden2):

super(Critic, self).__init__()

self.net = nn.Sequential(

nn.Linear(n_states + action_dim, hidden2),

nn.ReLU(),

nn.Linear(hidden2, hidden2),

nn.ReLU(),

nn.Linear(hidden2, hidden2),

nn.ReLU(),

nn.Linear(hidden2, action_dim)

)

def forward(self, state, action):

return self.net(torch.cat((state, action), 1))

Implementing Exploration Noise

Incorporating noise into the actions selected by the Actor is critical for encouraging exploration. Options include Gaussian noise or Ornstein-Uhlenbeck noise. The latter provides smoother fluctuations, enhancing exploration efficiency.

class OU_Noise:

"""Ornstein-Uhlenbeck process implementation."""

def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2):

self.mu = mu * np.ones(size)

self.theta = theta

self.sigma = sigma

self.seed = random.seed(seed)

self.reset()

def reset(self):

"""Reset the noise state to the mean."""

self.state = copy.copy(self.mu)

def sample(self):

"""Generate and return a new noise sample."""

dx = self.theta * (self.mu - self.state) + self.sigma * np.random.normal(size=len(self.state))

self.state += dx

return self.state

DDPG Algorithm Overview

DDPG utilizes two sets of Actor-Critic networks to approximate functions. The Target Network mirrors the Actor-Critic network but is updated more gradually through Soft Target updates, which enhance stability during training.

# Hyperparameters setup

capacity = 1000000

batch_size = 64

update_iteration = 200

tau = 0.001 # Soft update rate

gamma = 0.99 # Discount factor

directory = './'

hidden1 = 20 # Hidden layer size for Actor

hidden2 = 64 # Hidden layer size for Critic

class DDPG:

def __init__(self, state_dim, action_dim):

"""Initialize the DDPG agent."""

self.replay_buffer = ReplayBuffer()

self.actor = Actor(state_dim, action_dim, hidden1).to(device)

self.actor_target = Actor(state_dim, action_dim, hidden1).to(device)

self.actor_target.load_state_dict(self.actor.state_dict())

self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=3e-3)

self.critic = Critic(state_dim, action_dim, hidden2).to(device)

self.critic_target = Critic(state_dim, action_dim, hidden2).to(device)

self.critic_target.load_state_dict(self.critic.state_dict())

self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=2e-2)

self.num_critic_update_iteration = 0

self.num_actor_update_iteration = 0

self.num_training = 0

Training the Agent in the Mountain Car Environment

The following code demonstrates how to train the DDPG agent within the 'MountainCarContinuous-v0' environment, where the objective is for a car to reach the top of a mountain.

import gym

# Create the environment

env_name = 'MountainCarContinuous-v0'

env = gym.make(env_name)

device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Training parameters

max_episode = 100

max_time_steps = 5000

total_reward = 0

score_history = []

# Reproducibility

env.seed(0)

torch.manual_seed(0)

np.random.seed(0)

# Environment dimensions

state_dim = env.observation_space.shape[0]

action_dim = env.action_space.shape[0]

max_action = float(env.action_space.high[0])

# Instantiate the DDPG agent

agent = DDPG(state_dim, action_dim)

# Training loop

for i in range(max_episode):

state = env.reset()

for t in range(max_time_steps):

action = agent.select_action(state)

action = (action + np.random.normal(0, 1, size=action_dim)).clip(-max_action, max_action)

next_state, reward, done, _ = env.step(action)

agent.replay_buffer.push((state, next_state, action, reward, float(done)))

state = next_state

if done:

break

agent.update()

Testing the Trained DDPG Agent

After training, it's important to assess how well the agent performs in the environment.

test_iterations = 100

for i in range(test_iterations):

state = env.reset()

total_reward = 0

while True:

action = agent.select_action(state)

next_state, reward, done, _ = env.step(action)

total_reward += reward

if done:

print(f"Episode {i}, Total Reward: {total_reward}")

break

state = next_state

Conclusion

The DDPG algorithm is a powerful off-policy Actor-Critic method suitable for continuous action spaces. It leverages a replay buffer and a target network to stabilize training. Achieving optimal performance requires careful tuning of hyperparameters, as even slight adjustments can significantly influence the algorithm's effectiveness.

Video Resources

To further enhance your understanding of DDPG, check out the following YouTube tutorials:

This video, titled "Reinforcement Learning in Continuous Action Spaces | DDPG Tutorial (Pytorch)", provides an in-depth exploration of DDPG.

The second video, "How to Implement Deep Learning Papers | DDPG Tutorial", walks you through implementing DDPG in a practical context.

References

• OpenAI's Spinning Up
• David Silver's Course
• Berkeley Deep RL
• Practical RL