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import numpy as np
import torch as T
import torch.nn as nn
import torch.optim as optim
from torch.distributions import Categorical
class Agent:
def __init__(
self,
obs_space,
action_space,
hidden,
gamma,
lr,
value_coef,
entropy_coef,
seed,
lam
):
EPSILON = 1e-8
# Initialize seed for reproducibility
if seed is not None:
np.random.seed(seed)
T.manual_seed(seed)
# Use GPU if available
self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
self.action_dim = int(getattr(action_space, "n", action_space))
# Initialize the policy and the critic networks
self.policy = Policy(obs_space.shape, self.action_dim, hidden).to(self.device)
self.critic = Critic(obs_space.shape, hidden).to(self.device)
# Set optimizer for policy and critic networks
self.opt = optim.Adam(
list(self.policy.parameters()) + list(self.critic.parameters()),
lr=lr
)
self.gamma = gamma
self.value_coef = value_coef
self.entropy_coef = entropy_coef
self.sigma_history = []
self.loss_history = []
self.policy_loss_history = []
self.value_loss_history = []
self.entropy_history = []
self.lam = lam
self.EPSILON = EPSILON
self.observeNorm = ObservationNorm()
self.advantageNorm = AdvantageNorm()
self.returnNorm = ReturnNorm()
self.memory = Memory()
# Function to choose action based on current policy
# Returns: action, log probabilitiy, value of the state
def choose_action(self, observation):
state = T.as_tensor(observation, dtype=T.float32, device=self.device)
with T.no_grad():
dist = self.policy.next_action(state)
action = dist.sample()
value = self.critic.evaluated_state(state)
return int(action.item()), float(value.item())
# Store reward, state, action in memory
def remember(self, state, action, reward, done, value, next_state):
with T.no_grad():
ns = T.as_tensor(next_state, dtype=T.float32, device=self.device)
next_value = self.critic.evaluated_state(ns).item()
self.memory.store(state, action, reward, done, value, next_value)
def _prepare_batch_data(self):
"""Convert memory to tensors."""
states = T.as_tensor(np.array(self.memory.states), dtype=T.float32, device=self.device)
actions = T.as_tensor(self.memory.actions, dtype=T.long, device=self.device)
rewards = T.as_tensor(self.memory.rewards, dtype=T.float32, device=self.device)
dones = T.as_tensor(self.memory.dones, dtype=T.float32, device=self.device)
values = T.as_tensor(self.memory.values, dtype=T.float32, device=self.device)
return states, actions, rewards, dones, values
def _compute_gae(self, rewards, values, dones):
"""Compute Generalized Advantage Estimation."""
with T.no_grad():
next_values = T.cat([values[1:], values[-1:].clone()])
deltas = rewards + self.gamma * next_values * (1 - dones) - values
adv = T.zeros_like(rewards)
gae = 0.0
for t in reversed(range(len(rewards))):
gae = deltas[t] + self.gamma * self.lam * (1 - dones[t]) * gae
adv[t] = gae
returns = adv + values
return adv, returns
def _compute_a2c_loss(self, states, actions, returns, advantages):
"""Compute A2C loss components."""
dist = self.policy.next_action(states)
new_logp = dist.log_prob(actions)
entropy = dist.entropy().mean()
# Simple policy gradient (no clipping)
policy_loss = -(new_logp * advantages).mean()
# Critic loss
value_pred = self.critic.evaluated_state(states)
value_loss = 0.5 * (returns - value_pred).pow(2).mean()
# Total loss
total_loss = (
policy_loss +
self.value_coef * value_loss -
self.entropy_coef * entropy
)
return total_loss, policy_loss, value_loss
def _a2c_update(self, states, actions, returns, adv, use_grad_clip=False):
"""Run single A2C update (no multiple epochs)."""
total_loss, policy_loss, value_loss = self._compute_a2c_loss(
states, actions, returns, adv
)
self.policy_loss_history.append(policy_loss.item())
self.value_loss_history.append(value_loss.item())
self.opt.zero_grad(set_to_none=True)
total_loss.backward()
if use_grad_clip:
T.nn.utils.clip_grad_norm_(
list(self.policy.parameters()) + list(self.critic.parameters()),
0.5
)
self.opt.step()
return total_loss.item()
def vanilla_a2c_update(self):
if len(self.memory.states) == 0:
return 0.0
states, actions, rewards, dones, values = self._prepare_batch_data()
adv, returns = self._compute_gae(rewards, values, dones)
with T.no_grad():
adv = (adv - adv.mean()) / (adv.std(unbiased=False) + self.EPSILON)
avg_loss = self._a2c_update(states, actions, returns, adv) # changed
self.memory.clear()
return avg_loss
def update_rbs(self):
if len(self.memory.states) == 0:
return 0.0
states, actions, rewards, dones, values = self._prepare_batch_data()
adv, returns = self._compute_gae(rewards, values, dones)
with T.no_grad():
sigma_t = returns.std(unbiased=False) + 1e-8
returns = returns / sigma_t
self.sigma_history.append(sigma_t.item())
adv = adv / sigma_t
adv = (adv - adv.mean()) / (adv.std(unbiased=False) + 1e-8)
avg_loss = self._a2c_update(states, actions, returns, adv) # changed
self.memory.clear()
return avg_loss
def update_gradient_clipping(self):
if len(self.memory.states) == 0:
return 0.0
states, actions, rewards, dones, values = self._prepare_batch_data()
adv, returns = self._compute_gae(rewards, values, dones)
with T.no_grad():
adv = (adv - adv.mean()) / (adv.std(unbiased=False) + 1e-8)
avg_loss = self._a2c_update(states, actions, returns, adv, use_grad_clip=True) # changed
self.memory.clear()
return avg_loss
def update_obs_norm(self):
if len(self.memory.states) == 0:
return 0.0
states, actions, rewards, dones, values = self._prepare_batch_data()
adv, returns = self._compute_gae(rewards, values, dones)
with T.no_grad():
# --- observation normalization ---
states = self.observeNorm.normalize(states)
# Advantage normalization
adv = (adv - adv.mean()) / (adv.std(unbiased=False) + 1e-8)
avg_loss = self._a2c_update(states, actions, returns, adv)
self.memory.clear()
return avg_loss
def update_adv_norm(self):
if len(self.memory.states) == 0:
return 0.0
states, actions, rewards, dones, values = self._prepare_batch_data()
adv, returns = self._compute_gae(rewards, values, dones)
with T.no_grad():
# --- Advantage normalization ---
adv = self.advantageNorm.normalize(adv)
avg_loss = self._a2c_update(states, actions, returns, adv)
self.memory.clear()
return avg_loss
def update_return_norm(self):
if len(self.memory.states) == 0:
return 0.0
states = T.as_tensor(np.array(self.memory.states), dtype=T.float32, device=self.device)
actions = T.as_tensor(self.memory.actions, dtype=T.long, device=self.device)
rewards = T.as_tensor(self.memory.rewards, dtype=T.float32, device=self.device)
dones = T.as_tensor(self.memory.dones, dtype=T.float32, device=self.device)
values = T.as_tensor(self.memory.values, dtype=T.float32, device=self.device)
with T.no_grad():
next_values = T.cat([values[1:], values[-1:].clone()])
returns = rewards + self.gamma * next_values * (1 - dones)
adv = returns - values
returns = self.returnNorm.normalize(returns)
adv = (adv - adv.mean()) / (adv.std(unbiased=False) + 1e-8)
dist = self.policy.next_action(states)
log_probs = dist.log_prob(actions)
entropy = dist.entropy().mean()
policy_loss = -(log_probs * adv).mean()
value_pred = self.critic.evaluated_state(states)
value_loss = 0.5 * (returns - value_pred).pow(2).mean()
total_loss = (
policy_loss +
self.value_coef * value_loss -
self.entropy_coef * entropy
)
self.opt.zero_grad(set_to_none=True)
total_loss.backward()
self.opt.step()
avg_loss = self._a2c_update(states, actions, returns, adv)
self.memory.clear()
return avg_loss
def update_reward_norm(self):
if len(self.memory.states) == 0:
return 0.0
states = T.as_tensor(np.array(self.memory.states), dtype=T.float32, device=self.device)
actions = T.as_tensor(self.memory.actions, dtype=T.long, device=self.device)
rewards = T.as_tensor(self.memory.rewards, dtype=T.float32, device=self.device)
dones = T.as_tensor(self.memory.dones, dtype=T.float32, device=self.device)
values = T.as_tensor(self.memory.values, dtype=T.float32, device=self.device)
rewards = (rewards - rewards.mean()) / (rewards.std(unbiased=False) + 1e-8)
with T.no_grad():
next_values = T.cat([values[1:], values[-1:].clone()])
returns = rewards + self.gamma * next_values * (1 - dones)
adv = returns - values
adv = (adv - adv.mean()) / (adv.std(unbiased=False) + 1e-8)
dist = self.policy.next_action(states)
log_probs = dist.log_prob(actions)
entropy = dist.entropy().mean()
# Actor Loss: - log_prob * Advantage
policy_loss = -(log_probs * adv).mean()
value_pred = self.critic.evaluated_state(states)
value_loss = 0.5 * (returns - value_pred).pow(2).mean()
total_loss = (
policy_loss +
self.value_coef * value_loss -
self.entropy_coef * entropy
)
self.opt.zero_grad(set_to_none=True)
total_loss.backward()
self.opt.step()
avg_loss = self._a2c_update(states, actions, returns, adv)
self.memory.clear()
return avg_loss
# Policy network (CNN)
class Policy(nn.Module):
def __init__(self, obs_shape: tuple, action_dim: int, hidden: int):
super().__init__()
c, h, w = obs_shape
# Suggested architecture for Atari: https://arxiv.org/pdf/1312.5602
self.cnn = nn.Sequential(
nn.Conv2d(c, 16, kernel_size=8, stride=4),
nn.ReLU(),
nn.Conv2d(16, 32, kernel_size=4, stride=2),
nn.ReLU(),
nn.Flatten(),
nn.Linear(32 * 9 * 9, 256), # 2592 → 256
nn.ReLU(),
)
# Final output layer: one logit per action
self.net = nn.Linear(256, action_dim)
def next_action(self, state: T.Tensor) -> Categorical:
# state shape should be (B, C, H, W)
if state.dim() == 3:
state = state.unsqueeze(0)
cnn_out = self.cnn(state) # [B, 256]
logits = self.net(cnn_out) # [B, action_dim]
return Categorical(logits=logits)
# Critic network (CNN)
class Critic(nn.Module):
def __init__(self, obs_shape: tuple, hidden: int):
super().__init__()
c, h, w = obs_shape
# Suggested architecture for Atari: https://arxiv.org/pdf/1312.5602
self.cnn = nn.Sequential(
nn.Conv2d(c, 16, kernel_size=8, stride=4),
nn.ReLU(),
nn.Conv2d(16, 32, kernel_size=4, stride=2),
nn.ReLU(),
nn.Flatten()
)
with T.no_grad():
cnn_output_dim = self.cnn(T.zeros(1, c, h, w)).shape[1]
self.net = nn.Sequential(
nn.Linear(cnn_output_dim, hidden),
nn.ReLU(),
nn.Linear(hidden, 1)
)
def evaluated_state(self, x: T.Tensor) -> T.Tensor:
if x.dim() == 3:
x = x.unsqueeze(0)
cnn_out = self.cnn(x)
return self.net(cnn_out).squeeze(-1)
class Memory():
def __init__(self):
self.states = []
self.actions = []
self.rewards = []
self.dones = []
self.values = []
self.next_values = []
def store(self, state, action, reward, done, value, next_value):
self.states.append(np.asarray(state, dtype=np.float32))
self.actions.append(int(action))
self.rewards.append(float(reward))
self.dones.append(float(done))
self.values.append(float(value))
self.next_values.append(float(next_value))
def clear(self):
self.states = []
self.actions = []
self.rewards = []
self.dones = []
self.values = []
self.next_values = []
class ObservationNorm:
def normalize(self, x):
return (x - x.mean()) / (x.std(unbiased=False) + 1e-8) # We add epsilon to make sure that we don't
# divide through zero.
class AdvantageNorm:
'''
This class implements the Advantage Normalization. The purpose is to normalize either across batches or
only within the same batch.
'''
def normalize(self, x):
return (x - x.mean()) / (x.std(unbiased=False) + 1e-8) # We add epsilon to make sure that we don't
# divide through zero.
class ReturnNorm:
'''
This class implements the Return Normalization. The purpose is to normalize either across batches or
only within the same batch.
'''
def normalize(self, x):
return (x - x.mean()) / (x.std(unbiased=False) + 1e-8)
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