Okay, here’s a general guide for modeling and training Reinforcement Learning (RL) agents using PyTorch. This guide will cover the core components and steps, assuming you have a basic understanding of RL concepts (agent, environment, state, action, reward).

Core RL Components in PyTorch

  1. Environment:

    • Typically, you’ll use a library like gymnasium (the maintained fork of OpenAI Gym).
    • Key methods: env.reset(), env.step(action), env.render(), env.close().
    • Key attributes: env.observation_space, env.action_space.

  2. Agent: The learning entity. It usually consists of:

    • Policy Network (Actor): Maps states to actions (or probabilities of actions).
      • Implemented as a torch.nn.Module.
      • Output layer depends on action space:
        • Discrete: nn.Linear followed by torch.softmax (or logits for Categorical distribution).
        • Continuous: nn.Linear outputting mean (and optionally std dev) for a Normal distribution, often with torch.tanh to bound actions.
    • Value Network (Critic): Estimates the expected return (value) from a given state or state-action pair.
      • Implemented as a torch.nn.Module.
      • Output is typically a single scalar value.
    • Memory/Replay Buffer: Stores past experiences (state, action, reward, next_state, done) for off-policy learning (e.g., DQN, DDPG).
      • Can be a simple Python collections.deque or a more optimized custom class.
    • Optimizer: torch.optim (e.g., Adam, SGD) to update network weights.

General Workflow

Initialize Environment
Initialize Agent (Policy/Value Networks, Optimizer, Replay Buffer if needed)

FOR episode = 1 to N_EPISODES:
    Reset environment to get initial state (s)
    Initialize episode reward = 0

    FOR t = 1 to MAX_TIMESTEPS_PER_EPISODE:
        1. CHOOSE ACTION (a):
           - Get action from agent's policy network based on state (s)
           - Add exploration (e.g., epsilon-greedy, noise)

        2. INTERACT WITH ENVIRONMENT:
           - Take action (a) in environment: next_state (s'), reward (r), done, info = env.step(a)

        3. STORE EXPERIENCE (if applicable for the algorithm):
           - Store (s, a, r, s', done) in replay buffer

        4. UPDATE AGENT (LEARN):
           - If enough experiences are collected (or at every step for on-policy):
             - Sample a batch of experiences from buffer (off-policy) or use current trajectory (on-policy)
             - Calculate loss based on the chosen RL algorithm
             - optimizer.zero_grad()
             - loss.backward()
             - optimizer.step()
             - Update target networks (if applicable, e.g., DQN, DDPG)

        5. PREPARE FOR NEXT TIMESTEP:
           - s = s'
           - episode_reward += r

        IF done:
            break  // End of episode

    Log episode reward, length, and other metrics
    (Optional) Evaluate agent periodically without exploration

Close environment

Modeling with PyTorch (Key Parts)

  1. Defining Networks (torch.nn.Module)

     1import torch
 2import torch.nn as nn
 3import torch.nn.functional as F
 4import torch.optim as optim
 5import gymnasium as gym
 6import numpy as np
 7from collections import deque
 8import random
 9
10# Example: Policy Network for Discrete Actions (Actor)
11class PolicyNetwork(nn.Module):
12    def __init__(self, state_dim, action_dim, hidden_dim=64):
13        super(PolicyNetwork, self).__init__()
14        self.fc1 = nn.Linear(state_dim, hidden_dim)
15        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
16        self.fc_policy = nn.Linear(hidden_dim, action_dim) # Outputs logits for actions
17
18    def forward(self, state):
19        x = F.relu(self.fc1(state))
20        x = F.relu(self.fc2(x))
21        action_logits = self.fc_policy(x)
22        return action_logits # Return logits, apply softmax externally if needed for sampling
23
24# Example: Value Network (Critic)
25class ValueNetwork(nn.Module):
26    def __init__(self, state_dim, hidden_dim=64):
27        super(ValueNetwork, self).__init__()
28        self.fc1 = nn.Linear(state_dim, hidden_dim)
29        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
30        self.fc_value = nn.Linear(hidden_dim, 1) # Outputs a single value
31
32    def forward(self, state):
33        x = F.relu(self.fc1(state))
34        x = F.relu(self.fc2(x))
35        value = self.fc_value(x)
36        return value

  2. Choosing Actions

    • Discrete:
      1# policy_net is an instance of PolicyNetwork
2state_tensor = torch.FloatTensor(state).unsqueeze(0) # Add batch dimension
3with torch.no_grad(): # No gradient needed for action selection
4    action_logits = policy_net(state_tensor)
5    action_probs = F.softmax(action_logits, dim=-1)
6    action_distribution = torch.distributions.Categorical(action_probs)
7    action = action_distribution.sample()
8    log_prob = action_distribution.log_prob(action) # Needed for policy gradients
9# action.item() is the chosen action
    • Continuous (e.g., outputting mean and std for Gaussian):
      1# policy_net_continuous outputs (mean, log_std)
2# state_tensor = torch.FloatTensor(state).unsqueeze(0)
3# with torch.no_grad():
4#     mean, log_std = policy_net_continuous(state_tensor)
5#     std = torch.exp(log_std)
6#     normal_dist = torch.distributions.Normal(mean, std)
7#     action = normal_dist.sample()
8#     action = torch.tanh(action) # Often used to bound actions to [-1, 1]
9# # action.numpy() or action.squeeze().numpy()

  3. Replay Buffer (Example for DQN-like algorithms)

     1class ReplayBuffer:
 2    def __init__(self, capacity):
 3        self.buffer = deque(maxlen=capacity)
 4
 5    def push(self, state, action, reward, next_state, done):
 6        experience = (state, action, reward, next_state, done)
 7        self.buffer.append(experience)
 8
 9    def sample(self, batch_size):
10        batch = random.sample(self.buffer, batch_size)
11        states, actions, rewards, next_states, dones = zip(*batch)
12        return (np.array(states), np.array(actions), np.array(rewards),
13                np.array(next_states), np.array(dones))
14
15    def __len__(self):
16        return len(self.buffer)

Training (Algorithm-Specific Losses)

This is where different RL algorithms diverge significantly.

  • Deep Q-Network (DQN) - Value-Based (Off-Policy)

    • Networks: Q-Network (q_net), Target Q-Network (target_q_net).
    • Loss: Mean Squared Bellman Error (MSBE)
       1# states, actions, rewards, next_states, dones are Tensors
 2# q_net and target_q_net are instances of a Q-value network
 3# (similar to ValueNetwork but outputs Q-values for all actions)
 4
 5current_q_values = q_net(states).gather(1, actions.unsqueeze(-1))
 6
 7with torch.no_grad(): # Target values should not contribute to gradient
 8    next_q_values = target_q_net(next_states).max(1)[0] # Max Q-value for next state
 9    # if done, target is just reward, else reward + gamma * next_q_value
10    target_q_values = rewards + (gamma * next_q_values * (1 - dones_float))
11
12loss = F.mse_loss(current_q_values.squeeze(-1), target_q_values)
    • Target Network Update: Periodically copy q_net weights to target_q_net (hard update) or use soft updates (polyak averaging).

  • REINFORCE (Policy Gradient) - Policy-Based (On-Policy)

    • Networks: Policy Network.
    • Loss:
       1# log_probs: list of log_prob(action) for each step in an episode
 2# rewards: list of rewards for each step in an episode
 3# gamma: discount factor
 4
 5returns = []
 6discounted_reward = 0
 7for r in reversed(rewards): # Calculate discounted returns G_t
 8    discounted_reward = r + gamma * discounted_reward
 9    returns.insert(0, discounted_reward)
10
11returns = torch.tensor(returns)
12# Normalize returns (optional but often helpful)
13returns = (returns - returns.mean()) / (returns.std() + 1e-9)
14
15policy_loss = []
16for log_prob, R in zip(log_probs, returns):
17    policy_loss.append(-log_prob * R) # Negative for gradient ascent
18
19loss = torch.stack(policy_loss).sum()

  • Actor-Critic (e.g., A2C) - Actor-Critic (On-Policy)

    • Networks: Policy Network (Actor), Value Network (Critic).
    • Actor Loss (Policy Gradient using Advantage):
       1# state_values: V(s_t) from Critic
 2# rewards, next_state_values, dones
 3# log_probs: log_prob(a_t | s_t) from Actor
 4
 5advantages = []
 6for i in range(len(rewards)):
 7    # Simplified GAE(0) or TD(0) advantage
 8    # For GAE(lambda) it's more complex
 9    td_target = rewards[i] + gamma * next_state_values[i] * (1 - dones[i])
10    advantage = td_target - state_values[i]
11    advantages.append(advantage)
12advantages = torch.tensor(advantages).detach() # Detach, critic error shouldn't flow to actor via advantage
13
14actor_loss = (-log_probs * advantages).mean()
    • Critic Loss (MSE for Value Function):
      1# td_target calculated as above
2critic_loss = F.mse_loss(state_values, td_target.detach())
    • Total Loss: loss = actor_loss + critic_loss_coefficient * critic_loss (+ optional entropy_bonus)

Important Considerations:

  1. Device Management: Use device = torch.device("cuda" if torch.cuda.is_available() else "cpu") and move networks and tensors to this device (model.to(device), tensor.to(device)).
  2. Hyperparameter Tuning: Crucial. Learning rate, discount factor (gamma), batch size, network architecture, exploration parameters, replay buffer size, update frequencies, etc.
  3. Exploration vs. Exploitation:
    • Epsilon-greedy: For discrete actions (DQN).
    • Adding noise to actions: For continuous actions (DDPG).
    • Entropy bonus: Encourage exploration in policy gradient methods.
  4. Normalization:
    • States: Often beneficial to normalize or standardize input states.
    • Rewards: Can sometimes help, e.g., scaling or clipping.
    • Advantages (in Policy Gradients): Standardizing advantages often stabilizes training.
  5. Evaluation: Periodically evaluate the agent’s performance using a deterministic policy (no exploration noise/epsilon) on a set of evaluation episodes.
  6. Logging: Use tools like TensorBoard (torch.utils.tensorboard.SummaryWriter) or Weights & Biases (wandb) to track metrics (rewards, losses, episode lengths).
  7. Reproducibility: Set random seeds for random, numpy, torch, and the environment.
    1seed = 42
2random.seed(seed)
3np.random.seed(seed)
4torch.manual_seed(seed)
5if torch.cuda.is_available():
6    torch.cuda.manual_seed_all(seed)
7# env.seed(seed) # for older gym
8# env.action_space.seed(seed) # for newer gymnasium

PyTorch-Derivative RL Libraries:

While implementing from scratch is great for learning, for more complex projects or using SOTA algorithms, consider these libraries built on PyTorch:

  • Stable Baselines3 (SB3): Very popular, well-maintained, offers pre-implemented common RL algorithms (DQN, PPO, SAC, A2C, DDPG, TD3). Excellent for benchmarking and getting started quickly.
  • RLlib (from Ray): Focuses on scalability and distributed RL. More complex but powerful.
  • Tianshou: A highly flexible and research-oriented library offering a wide range of algorithms and features.
  • TorchRL (from PyTorch Ecosystem): A newer library aiming to provide modular building blocks for RL directly within the PyTorch ecosystem. Supports tensordict for efficient data handling.

Getting Started Example (Conceptual - DQN on CartPole)

 1# 1. Setup
 2env = gym.make("CartPole-v1")
 3state_dim = env.observation_space.shape[0]
 4action_dim = env.action_space.n # Discrete
 5
 6q_net = QNetwork(state_dim, action_dim).to(device) # QNetwork similar to PolicyNetwork but outputting Q-values for all actions
 7target_q_net = QNetwork(state_dim, action_dim).to(device)
 8target_q_net.load_state_dict(q_net.state_dict()) # Initialize target same as main
 9optimizer = optim.Adam(q_net.parameters(), lr=0.001)
10replay_buffer = ReplayBuffer(capacity=10000)
11gamma = 0.99
12epsilon_start = 1.0
13epsilon_end = 0.01
14epsilon_decay = 0.995
15batch_size = 64
16target_update_freq = 10 # Update target network every 10 episodes or steps
17
18# 2. Training Loop
19epsilon = epsilon_start
20for episode in range(500):
21    state, _ = env.reset()
22    episode_reward = 0
23    done = False
24    truncated = False # Gymnasium specific
25
26    while not done and not truncated:
27        # Choose action (epsilon-greedy)
28        if random.random() < epsilon:
29            action = env.action_space.sample()
30        else:
31            state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)
32            with torch.no_grad():
33                q_values = q_net(state_tensor)
34                action = q_values.argmax(dim=1).item()
35
36        next_state, reward, done, truncated, _ = env.step(action)
37        replay_buffer.push(state, action, reward, next_state, float(done)) # Store done as float
38        episode_reward += reward
39        state = next_state
40
41        # Learn (if buffer has enough samples)
42        if len(replay_buffer) > batch_size:
43            states_b, actions_b, rewards_b, next_states_b, dones_b = replay_buffer.sample(batch_size)
44
45            states_t = torch.FloatTensor(states_b).to(device)
46            actions_t = torch.LongTensor(actions_b).unsqueeze(1).to(device) # DQN needs LongTensor for gather
47            rewards_t = torch.FloatTensor(rewards_b).to(device)
48            next_states_t = torch.FloatTensor(next_states_b).to(device)
49            dones_t = torch.FloatTensor(dones_b).to(device)
50
51            current_q = q_net(states_t).gather(1, actions_t)
52
53            with torch.no_grad():
54                next_q_target = target_q_net(next_states_t).max(1)[0]
55                expected_q = rewards_t + gamma * next_q_target * (1 - dones_t)
56
57            loss = F.mse_loss(current_q.squeeze(1), expected_q)
58
59            optimizer.zero_grad()
60            loss.backward()
61            optimizer.step()
62
63    epsilon = max(epsilon_end, epsilon * epsilon_decay) # Decay epsilon
64
65    if episode % target_update_freq == 0:
66        target_q_net.load_state_dict(q_net.state_dict())
67
68    print(f"Episode: {episode}, Reward: {episode_reward}, Epsilon: {epsilon:.2f}")
69
70env.close()

This guide provides a foundational structure. Each RL algorithm has its nuances, but the PyTorch components (networks, optimizers, tensor operations) remain consistent. Start with simpler algorithms like DQN or REINFORCE on classic control environments (CartPole-v1, LunarLander-v2) before tackling more complex ones. Good luck!