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Zhongwei Li
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skills/pufferlib/references/policies.md
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skills/pufferlib/references/policies.md
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# PufferLib Policies Guide
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## Overview
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PufferLib policies are standard PyTorch modules with optional utilities for observation processing and LSTM integration. The framework provides default architectures and tools while allowing full flexibility in policy design.
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## Policy Architecture
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### Basic Policy Structure
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```python
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import torch
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import torch.nn as nn
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from pufferlib.pytorch import layer_init
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class BasicPolicy(nn.Module):
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def __init__(self, observation_space, action_space):
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super().__init__()
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self.observation_space = observation_space
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self.action_space = action_space
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# Encoder network
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self.encoder = nn.Sequential(
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layer_init(nn.Linear(observation_space.shape[0], 256)),
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nn.ReLU(),
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layer_init(nn.Linear(256, 256)),
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nn.ReLU()
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)
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# Policy head (actor)
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self.actor = layer_init(nn.Linear(256, action_space.n), std=0.01)
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# Value head (critic)
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self.critic = layer_init(nn.Linear(256, 1), std=1.0)
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def forward(self, observations):
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"""Forward pass through policy."""
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# Encode observations
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features = self.encoder(observations)
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# Get action logits and value
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logits = self.actor(features)
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value = self.critic(features)
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return logits, value
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def get_action(self, observations, deterministic=False):
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"""Sample action from policy."""
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logits, value = self.forward(observations)
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if deterministic:
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action = logits.argmax(dim=-1)
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else:
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dist = torch.distributions.Categorical(logits=logits)
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action = dist.sample()
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return action, value
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```
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### Layer Initialization
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PufferLib provides `layer_init` for proper weight initialization:
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```python
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from pufferlib.pytorch import layer_init
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# Default orthogonal initialization
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layer = layer_init(nn.Linear(256, 256))
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# Custom standard deviation
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actor_head = layer_init(nn.Linear(256, num_actions), std=0.01)
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critic_head = layer_init(nn.Linear(256, 1), std=1.0)
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# Works with any layer type
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conv = layer_init(nn.Conv2d(3, 32, kernel_size=8, stride=4))
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```
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## CNN Policies
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For image-based observations:
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```python
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class CNNPolicy(nn.Module):
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def __init__(self, observation_space, action_space):
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super().__init__()
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# CNN encoder for images
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self.encoder = nn.Sequential(
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layer_init(nn.Conv2d(3, 32, kernel_size=8, stride=4)),
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nn.ReLU(),
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layer_init(nn.Conv2d(32, 64, kernel_size=4, stride=2)),
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nn.ReLU(),
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layer_init(nn.Conv2d(64, 64, kernel_size=3, stride=1)),
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nn.ReLU(),
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nn.Flatten(),
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layer_init(nn.Linear(64 * 7 * 7, 512)),
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nn.ReLU()
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)
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self.actor = layer_init(nn.Linear(512, action_space.n), std=0.01)
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self.critic = layer_init(nn.Linear(512, 1), std=1.0)
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def forward(self, observations):
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# Normalize pixel values
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x = observations.float() / 255.0
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features = self.encoder(x)
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logits = self.actor(features)
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value = self.critic(features)
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return logits, value
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```
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### Efficient CNN Architecture
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```python
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class EfficientCNN(nn.Module):
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"""Optimized CNN for Atari-style games."""
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def __init__(self, observation_space, action_space):
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super().__init__()
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in_channels = observation_space.shape[0] # Typically 4 for framestack
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self.network = nn.Sequential(
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layer_init(nn.Conv2d(in_channels, 32, 8, stride=4)),
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nn.ReLU(),
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layer_init(nn.Conv2d(32, 64, 4, stride=2)),
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nn.ReLU(),
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layer_init(nn.Conv2d(64, 64, 3, stride=1)),
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nn.ReLU(),
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nn.Flatten()
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)
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# Calculate feature size
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with torch.no_grad():
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sample = torch.zeros(1, *observation_space.shape)
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n_features = self.network(sample).shape[1]
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self.fc = layer_init(nn.Linear(n_features, 512))
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self.actor = layer_init(nn.Linear(512, action_space.n), std=0.01)
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self.critic = layer_init(nn.Linear(512, 1), std=1.0)
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def forward(self, x):
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x = x.float() / 255.0
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x = self.network(x)
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x = torch.relu(self.fc(x))
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return self.actor(x), self.critic(x)
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```
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## Recurrent Policies (LSTM)
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PufferLib provides optimized LSTM integration with automatic recurrence handling:
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```python
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from pufferlib.pytorch import LSTMWrapper
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class RecurrentPolicy(nn.Module):
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def __init__(self, observation_space, action_space, hidden_size=256):
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super().__init__()
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# Observation encoder
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self.encoder = nn.Sequential(
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layer_init(nn.Linear(observation_space.shape[0], 128)),
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nn.ReLU()
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)
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# LSTM layer
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self.lstm = nn.LSTM(128, hidden_size, num_layers=1)
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# Policy and value heads
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self.actor = layer_init(nn.Linear(hidden_size, action_space.n), std=0.01)
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self.critic = layer_init(nn.Linear(hidden_size, 1), std=1.0)
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# Hidden state
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self.hidden_size = hidden_size
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def forward(self, observations, state=None):
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"""
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Args:
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observations: (batch, obs_dim)
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state: Optional (h, c) tuple for LSTM
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Returns:
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logits, value, new_state
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"""
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batch_size = observations.shape[0]
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# Encode observations
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features = self.encoder(observations)
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# Initialize hidden state if needed
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if state is None:
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h = torch.zeros(1, batch_size, self.hidden_size, device=features.device)
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c = torch.zeros(1, batch_size, self.hidden_size, device=features.device)
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state = (h, c)
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# LSTM forward
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features = features.unsqueeze(0) # Add sequence dimension
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lstm_out, new_state = self.lstm(features, state)
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lstm_out = lstm_out.squeeze(0)
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# Get outputs
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logits = self.actor(lstm_out)
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value = self.critic(lstm_out)
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return logits, value, new_state
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```
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### LSTM Optimization
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PufferLib's LSTM optimization uses LSTMCell during rollouts and LSTM during training for up to 3x faster inference:
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```python
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class OptimizedLSTMPolicy(nn.Module):
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def __init__(self, observation_space, action_space, hidden_size=256):
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super().__init__()
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self.encoder = nn.Sequential(
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layer_init(nn.Linear(observation_space.shape[0], 128)),
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nn.ReLU()
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)
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# Use LSTMCell for step-by-step inference
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self.lstm_cell = nn.LSTMCell(128, hidden_size)
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# Use LSTM for batch training
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self.lstm = nn.LSTM(128, hidden_size, num_layers=1)
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self.actor = layer_init(nn.Linear(hidden_size, action_space.n), std=0.01)
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self.critic = layer_init(nn.Linear(hidden_size, 1), std=1.0)
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self.hidden_size = hidden_size
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def encode_observations(self, observations, state):
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"""Fast inference using LSTMCell."""
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features = self.encoder(observations)
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if state is None:
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h = torch.zeros(observations.shape[0], self.hidden_size, device=features.device)
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c = torch.zeros(observations.shape[0], self.hidden_size, device=features.device)
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else:
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h, c = state
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# Step-by-step with LSTMCell (faster for inference)
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h, c = self.lstm_cell(features, (h, c))
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logits = self.actor(h)
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value = self.critic(h)
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return logits, value, (h, c)
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def decode_actions(self, observations, actions, state):
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"""Batch training using LSTM."""
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seq_len, batch_size = observations.shape[:2]
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# Reshape for LSTM
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obs_flat = observations.reshape(seq_len * batch_size, -1)
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features = self.encoder(obs_flat)
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features = features.reshape(seq_len, batch_size, -1)
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if state is None:
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h = torch.zeros(1, batch_size, self.hidden_size, device=features.device)
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c = torch.zeros(1, batch_size, self.hidden_size, device=features.device)
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state = (h, c)
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# Batch processing with LSTM (faster for training)
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lstm_out, new_state = self.lstm(features, state)
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# Flatten back
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lstm_out = lstm_out.reshape(seq_len * batch_size, -1)
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logits = self.actor(lstm_out)
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value = self.critic(lstm_out)
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return logits, value, new_state
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```
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## Multi-Input Policies
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For environments with multiple observation types:
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```python
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class MultiInputPolicy(nn.Module):
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def __init__(self, observation_space, action_space):
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super().__init__()
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# Separate encoders for different observation types
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self.image_encoder = nn.Sequential(
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layer_init(nn.Conv2d(3, 32, 8, stride=4)),
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nn.ReLU(),
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layer_init(nn.Conv2d(32, 64, 4, stride=2)),
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nn.ReLU(),
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nn.Flatten()
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)
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self.vector_encoder = nn.Sequential(
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layer_init(nn.Linear(observation_space['vector'].shape[0], 128)),
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nn.ReLU()
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)
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# Combine features
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combined_size = 64 * 9 * 9 + 128 # Image features + vector features
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self.combiner = nn.Sequential(
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layer_init(nn.Linear(combined_size, 512)),
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nn.ReLU()
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)
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self.actor = layer_init(nn.Linear(512, action_space.n), std=0.01)
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self.critic = layer_init(nn.Linear(512, 1), std=1.0)
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def forward(self, observations):
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# Process each observation type
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image_features = self.image_encoder(observations['image'].float() / 255.0)
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vector_features = self.vector_encoder(observations['vector'])
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# Combine
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combined = torch.cat([image_features, vector_features], dim=-1)
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features = self.combiner(combined)
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return self.actor(features), self.critic(features)
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```
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## Continuous Action Policies
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For continuous control tasks:
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```python
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class ContinuousPolicy(nn.Module):
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def __init__(self, observation_space, action_space):
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super().__init__()
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self.encoder = nn.Sequential(
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layer_init(nn.Linear(observation_space.shape[0], 256)),
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nn.ReLU(),
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layer_init(nn.Linear(256, 256)),
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nn.ReLU()
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)
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# Mean of action distribution
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self.actor_mean = layer_init(nn.Linear(256, action_space.shape[0]), std=0.01)
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# Log std of action distribution
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self.actor_logstd = nn.Parameter(torch.zeros(1, action_space.shape[0]))
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# Value head
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self.critic = layer_init(nn.Linear(256, 1), std=1.0)
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def forward(self, observations):
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features = self.encoder(observations)
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action_mean = self.actor_mean(features)
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action_std = torch.exp(self.actor_logstd)
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value = self.critic(features)
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return action_mean, action_std, value
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def get_action(self, observations, deterministic=False):
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action_mean, action_std, value = self.forward(observations)
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if deterministic:
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return action_mean, value
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else:
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dist = torch.distributions.Normal(action_mean, action_std)
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action = dist.sample()
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return torch.tanh(action), value # Bound actions to [-1, 1]
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```
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## Observation Processing
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PufferLib provides utilities for unflattening observations:
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```python
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from pufferlib.pytorch import unflatten_observations
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class PolicyWithUnflatten(nn.Module):
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def __init__(self, observation_space, action_space):
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super().__init__()
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self.observation_space = observation_space
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# Define encoders for each observation component
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self.encoders = nn.ModuleDict({
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'image': self._make_image_encoder(),
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'vector': self._make_vector_encoder()
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})
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# ... rest of policy ...
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def forward(self, flat_observations):
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# Unflatten observations into structured format
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observations = unflatten_observations(
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flat_observations,
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self.observation_space
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)
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# Process each component
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image_features = self.encoders['image'](observations['image'])
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vector_features = self.encoders['vector'](observations['vector'])
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# Combine and continue...
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```
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## Multi-Agent Policies
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### Shared Parameters
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All agents use the same policy:
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```python
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class SharedMultiAgentPolicy(nn.Module):
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def __init__(self, observation_space, action_space, num_agents):
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super().__init__()
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self.num_agents = num_agents
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# Single policy shared across all agents
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self.encoder = nn.Sequential(
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layer_init(nn.Linear(observation_space.shape[0], 256)),
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nn.ReLU()
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)
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self.actor = layer_init(nn.Linear(256, action_space.n), std=0.01)
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self.critic = layer_init(nn.Linear(256, 1), std=1.0)
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def forward(self, observations):
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"""
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Args:
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observations: (batch * num_agents, obs_dim)
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Returns:
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logits: (batch * num_agents, num_actions)
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values: (batch * num_agents, 1)
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"""
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features = self.encoder(observations)
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return self.actor(features), self.critic(features)
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```
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### Independent Parameters
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Each agent has its own policy:
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```python
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class IndependentMultiAgentPolicy(nn.Module):
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def __init__(self, observation_space, action_space, num_agents):
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super().__init__()
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self.num_agents = num_agents
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# Separate policy for each agent
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self.policies = nn.ModuleList([
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self._make_policy(observation_space, action_space)
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for _ in range(num_agents)
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])
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def _make_policy(self, observation_space, action_space):
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return nn.Sequential(
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layer_init(nn.Linear(observation_space.shape[0], 256)),
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nn.ReLU(),
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layer_init(nn.Linear(256, 256)),
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nn.ReLU()
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)
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def forward(self, observations, agent_ids):
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"""
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Args:
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observations: (batch, obs_dim)
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agent_ids: (batch,) which agent each obs belongs to
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"""
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outputs = []
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for agent_id in range(self.num_agents):
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mask = agent_ids == agent_id
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if mask.any():
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agent_obs = observations[mask]
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agent_out = self.policies[agent_id](agent_obs)
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outputs.append(agent_out)
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return torch.cat(outputs, dim=0)
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```
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## Advanced Architectures
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### Attention-Based Policy
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```python
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class AttentionPolicy(nn.Module):
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def __init__(self, observation_space, action_space, d_model=256, nhead=8):
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super().__init__()
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self.encoder = layer_init(nn.Linear(observation_space.shape[0], d_model))
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self.attention = nn.MultiheadAttention(d_model, nhead, batch_first=True)
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self.actor = layer_init(nn.Linear(d_model, action_space.n), std=0.01)
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self.critic = layer_init(nn.Linear(d_model, 1), std=1.0)
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||||
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||||
def forward(self, observations):
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# Encode
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features = self.encoder(observations)
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||||
# Self-attention
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||||
features = features.unsqueeze(1) # Add sequence dimension
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attn_out, _ = self.attention(features, features, features)
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attn_out = attn_out.squeeze(1)
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||||
|
||||
return self.actor(attn_out), self.critic(attn_out)
|
||||
```
|
||||
|
||||
### Residual Policy
|
||||
|
||||
```python
|
||||
class ResidualBlock(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.block = nn.Sequential(
|
||||
layer_init(nn.Linear(dim, dim)),
|
||||
nn.ReLU(),
|
||||
layer_init(nn.Linear(dim, dim))
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.block(x)
|
||||
|
||||
class ResidualPolicy(nn.Module):
|
||||
def __init__(self, observation_space, action_space, num_blocks=4):
|
||||
super().__init__()
|
||||
|
||||
dim = 256
|
||||
|
||||
self.encoder = layer_init(nn.Linear(observation_space.shape[0], dim))
|
||||
|
||||
self.blocks = nn.Sequential(
|
||||
*[ResidualBlock(dim) for _ in range(num_blocks)]
|
||||
)
|
||||
|
||||
self.actor = layer_init(nn.Linear(dim, action_space.n), std=0.01)
|
||||
self.critic = layer_init(nn.Linear(dim, 1), std=1.0)
|
||||
|
||||
def forward(self, observations):
|
||||
x = torch.relu(self.encoder(observations))
|
||||
x = self.blocks(x)
|
||||
return self.actor(x), self.critic(x)
|
||||
```
|
||||
|
||||
## Policy Best Practices
|
||||
|
||||
### Initialization
|
||||
|
||||
```python
|
||||
# Always use layer_init for proper initialization
|
||||
good_layer = layer_init(nn.Linear(256, 256))
|
||||
|
||||
# Use small std for actor head (more stable early training)
|
||||
actor = layer_init(nn.Linear(256, num_actions), std=0.01)
|
||||
|
||||
# Use std=1.0 for critic head
|
||||
critic = layer_init(nn.Linear(256, 1), std=1.0)
|
||||
```
|
||||
|
||||
### Observation Normalization
|
||||
|
||||
```python
|
||||
class NormalizedPolicy(nn.Module):
|
||||
def __init__(self, observation_space, action_space):
|
||||
super().__init__()
|
||||
|
||||
# Running statistics for normalization
|
||||
self.obs_mean = nn.Parameter(torch.zeros(observation_space.shape[0]), requires_grad=False)
|
||||
self.obs_std = nn.Parameter(torch.ones(observation_space.shape[0]), requires_grad=False)
|
||||
|
||||
# ... rest of policy ...
|
||||
|
||||
def forward(self, observations):
|
||||
# Normalize observations
|
||||
normalized_obs = (observations - self.obs_mean) / (self.obs_std + 1e-8)
|
||||
|
||||
# Continue with normalized observations
|
||||
return self.policy(normalized_obs)
|
||||
|
||||
def update_normalization(self, observations):
|
||||
"""Update running statistics."""
|
||||
self.obs_mean.data = observations.mean(dim=0)
|
||||
self.obs_std.data = observations.std(dim=0)
|
||||
```
|
||||
|
||||
### Gradient Clipping
|
||||
|
||||
```python
|
||||
# PufferLib trainer handles gradient clipping automatically
|
||||
trainer = PuffeRL(
|
||||
env=env,
|
||||
policy=policy,
|
||||
max_grad_norm=0.5 # Clip gradients to this norm
|
||||
)
|
||||
```
|
||||
|
||||
### Model Compilation
|
||||
|
||||
```python
|
||||
# Enable torch.compile for faster training (PyTorch 2.0+)
|
||||
policy = MyPolicy(observation_space, action_space)
|
||||
|
||||
# Compile the model
|
||||
policy = torch.compile(policy, mode='reduce-overhead')
|
||||
|
||||
# Use with trainer
|
||||
trainer = PuffeRL(env=env, policy=policy, compile=True)
|
||||
```
|
||||
|
||||
## Debugging Policies
|
||||
|
||||
### Check Output Shapes
|
||||
|
||||
```python
|
||||
def test_policy_shapes(policy, observation_space, batch_size=32):
|
||||
"""Verify policy output shapes."""
|
||||
# Create dummy observations
|
||||
obs = torch.randn(batch_size, *observation_space.shape)
|
||||
|
||||
# Forward pass
|
||||
logits, value = policy(obs)
|
||||
|
||||
# Check shapes
|
||||
assert logits.shape == (batch_size, policy.action_space.n)
|
||||
assert value.shape == (batch_size, 1)
|
||||
|
||||
print("✓ Policy shapes correct")
|
||||
```
|
||||
|
||||
### Verify Gradients
|
||||
|
||||
```python
|
||||
def check_gradients(policy, observation_space):
|
||||
"""Check that gradients flow properly."""
|
||||
obs = torch.randn(1, *observation_space.shape, requires_grad=True)
|
||||
|
||||
logits, value = policy(obs)
|
||||
|
||||
# Backward pass
|
||||
loss = logits.sum() + value.sum()
|
||||
loss.backward()
|
||||
|
||||
# Check gradients exist
|
||||
for name, param in policy.named_parameters():
|
||||
if param.grad is None:
|
||||
print(f"⚠ No gradient for {name}")
|
||||
elif torch.isnan(param.grad).any():
|
||||
print(f"⚠ NaN gradient for {name}")
|
||||
else:
|
||||
print(f"✓ Gradient OK for {name}")
|
||||
```
|
||||
Reference in New Issue
Block a user