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76ab7ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions import Categorical
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions import Categorical
import numpy as np
class PolicyNetwork(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.net = nn.Sequential(
nn.Linear(state_dim, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, action_dim)
)
for layer in self.net:
if isinstance(layer, nn.Linear):
nn.init.xavier_uniform_(layer.weight)
nn.init.constant_(layer.bias, 0)
def forward(self, state):
return self.net(state)
class ValueNetwork(nn.Module):
def __init__(self, state_dim):
super().__init__()
self.net = nn.Sequential(
nn.Linear(state_dim, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 1)
)
for layer in self.net:
if isinstance(layer, nn.Linear):
nn.init.xavier_uniform_(layer.weight)
nn.init.constant_(layer.bias, 0)
def forward(self, state):
return self.net(state)
class PPOAgent:
def __init__(self, state_dim, action_dim, lr=3e-4, gamma=0.99, clip_eps=0.2):
self.policy = PolicyNetwork(state_dim, action_dim)
self.value = ValueNetwork(state_dim)
self.optimizer = optim.Adam(
list(self.policy.parameters()) + list(self.value.parameters()),
lr=lr
)
self.memory = []
self.gamma = gamma
self.clip_eps = clip_eps
self.epsilon = 1.0
self.epsilon_decay = 0.995
self.epsilon_min = 0.1
self.mini_batch_size = 256
self.update_epochs = 4
def select_action(self, state):
state = torch.FloatTensor(state).unsqueeze(0)
# Add input validation
if torch.isnan(state).any() or torch.isinf(state).any():
print(f"Warning: Invalid state detected: {state}")
action = np.random.randint(0, 8)
log_prob = torch.log(torch.tensor(1/8))
return action, log_prob
with torch.no_grad():
logits = self.policy(state)
if torch.isnan(logits).any() or torch.isinf(logits).any():
print(f"Warning: Invalid logits detected: {logits}")
action = np.random.randint(0, 8)
log_prob = torch.log(torch.tensor(1/8))
return action, log_prob
# Apply temperature scaling
logits = logits / 1.0
probs = torch.softmax(logits, dim=-1)
# Add small epsilon to prevent zero probabilities
probs = probs + 1e-8
probs = probs / probs.sum(dim=-1, keepdim=True)
dist = Categorical(probs)
action = dist.sample()
return action.item(), dist.log_prob(action)
def store_experience(self, state, action, reward, next_state, done, log_prob):
# Validate inputs before storing
if not (torch.isnan(torch.FloatTensor(state)).any() or
torch.isnan(torch.FloatTensor(next_state)).any() or
torch.isnan(log_prob).any() or
np.isnan(reward)):
self.memory.append((state, action, reward, next_state, done, log_prob))
def calculate_returns_and_advantages_optimized(self, rewards, values, dones):
"""
Optimized O(n) calculation of returns and advantages using GAE
"""
returns = torch.zeros_like(rewards)
advantages = torch.zeros_like(rewards)
# GAE parameters
gae_lambda = 0.95
# Calculate advantages and returns in reverse order (O(n))
last_gae = 0
for i in reversed(range(len(rewards))):
if i == len(rewards) - 1:
next_value = 0 # Terminal state
else:
next_value = values[i + 1]
# TD error
delta = rewards[i] + self.gamma * next_value - values[i]
# GAE advantage
advantages[i] = last_gae = delta + self.gamma * gae_lambda * last_gae
# Returns = advantages + values
returns[i] = advantages[i] + values[i]
return returns, advantages
def update(self, epochs=None):
if len(self.memory) < 32: # Minimum batch size
return None
# Use instance variable if epochs not specified
if epochs is None:
epochs = self.update_epochs
# Convert experience buffer to tensors
states, actions, rewards, next_states, dones, old_log_probs = zip(*self.memory)
states = torch.FloatTensor(np.array(states))
actions = torch.LongTensor(actions)
rewards = torch.FloatTensor(rewards)
old_log_probs = torch.stack(old_log_probs)
# Validate tensors
if (torch.isnan(states).any() or torch.isnan(rewards).any() or
torch.isnan(old_log_probs).any()):
print("Warning: NaN detected in experience buffer, skipping update")
self.memory.clear()
return None
# Get values for all states at once
with torch.no_grad():
values = self.value(states).squeeze(-1)
# OPTIMIZED: O(n) returns and advantages calculation
returns, advantages = self.calculate_returns_and_advantages_optimized(
rewards, values, torch.zeros_like(rewards) # dones placeholder
)
# Normalize advantages
if len(advantages) > 1 and advantages.std() > 1e-8:
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
# Mini-batch training for large datasets
dataset_size = len(states)
indices = torch.randperm(dataset_size)
total_policy_loss = 0
total_value_loss = 0
total_entropy = 0
update_count = 0
for epoch in range(epochs):
# Process in mini-batches to avoid memory issues
for start_idx in range(0, dataset_size, self.mini_batch_size):
end_idx = min(start_idx + self.mini_batch_size, dataset_size)
batch_indices = indices[start_idx:end_idx]
# Get mini-batch
batch_states = states[batch_indices]
batch_actions = actions[batch_indices]
batch_advantages = advantages[batch_indices]
batch_returns = returns[batch_indices]
batch_old_log_probs = old_log_probs[batch_indices]
# Forward pass
logits = self.policy(batch_states)
probs = torch.softmax(logits, dim=-1) + 1e-8
probs = probs / probs.sum(dim=-1, keepdim=True)
dist = Categorical(probs)
new_log_probs = dist.log_prob(batch_actions)
# Policy loss
ratio = torch.exp(new_log_probs - batch_old_log_probs)
ratio = torch.clamp(ratio, 0.1, 10.0)
surr1 = ratio * batch_advantages
surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * batch_advantages
policy_loss = -torch.min(surr1, surr2).mean()
# Value loss
current_values = self.value(batch_states).squeeze(-1)
value_loss = nn.MSELoss()(current_values, batch_returns)
# Entropy
entropy = dist.entropy().mean()
# Total loss
total_loss = policy_loss + 0.5 * value_loss - 0.01 * entropy # Reduced entropy coefficient
# Check for NaN
if torch.isnan(total_loss) or torch.isinf(total_loss):
print("Warning: NaN/Inf loss detected, skipping batch")
continue
# Optimize
self.optimizer.zero_grad()
total_loss.backward()
# Gradient clipping
torch.nn.utils.clip_grad_norm_(self.policy.parameters(), 0.5)
torch.nn.utils.clip_grad_norm_(self.value.parameters(), 0.5)
self.optimizer.step()
# Accumulate losses
total_policy_loss += policy_loss.detach().item()
total_value_loss += value_loss.detach().item()
total_entropy += entropy.detach().item()
update_count += 1
# Clear memory
self.memory.clear()
if update_count == 0:
return None
return {
'policy_loss': total_policy_loss / update_count,
'value_loss': total_value_loss / update_count,
'entropy': total_entropy / update_count
}
def save(self, filepath):
"""Save the model"""
torch.save({
'policy_state_dict': self.policy.state_dict(),
'value_state_dict': self.value.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict()
}, filepath)
def load(self, filepath):
"""Load the model"""
checkpoint = torch.load(filepath, map_location='cpu')
self.policy.load_state_dict(checkpoint['policy_state_dict'])
self.value.load_state_dict(checkpoint['value_state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict']) |