class PPO():
def __init__(self, state_dim, action_dim, num_shared, device):
self.state_dim = state_dim
self.action_dim = action_dim
self.device = device
self.actor = Actor(state_dim, action_dim, num_shared).to(device)
self.critic = Critic(state_dim, num_shared).to(device)
def parameters(self):
return itertools.chain(self.actor.parameters(), self.critic.parameters())
def first_parameters(self):
return itertools.chain(self.actor.first_parameters(), self.critic.first_parameters())
def shared_parameters(self):
return itertools.chain(self.actor.shared_parameters(), self.critic.shared_parameters())
def rest_parameters(self):
return itertools.chain(self.actor.rest_parameters(), self.critic.rest_parameters())
def _calc_loss(self, state, action, old_log_prob, expected_values, gae):
new_log_prob, action_distr = self.actor.compute_proba(state, action)
state_values = self.critic.get_value(state).squeeze(1)
critic_loss = ((expected_values - state_values) ** 2).mean()
unclipped_ratio = torch.exp(new_log_prob - old_log_prob)
clipped_ratio = torch.clamp(unclipped_ratio, 1 - CLIP, 1 + CLIP)
actor_loss = -torch.min(clipped_ratio * gae, unclipped_ratio * gae).mean()
entropy_loss = -action_distr.entropy().mean()
return critic_loss * VALUE_COEFF + actor_loss + entropy_loss * ENTROPY_COEF
def update(self, trajectories):
trajectories = map(self._compute_lambda_returns_and_gae, trajectories)
transitions = sum(trajectories, []) # Turn a list of trajectories into list of transitions
state, action, old_log_prob, target_value, advantage = zip(*transitions)
state = torch.from_numpy(np.array(state)).float().to(self.device)
action = torch.from_numpy(np.array(action)).float().to(self.device)
old_log_prob = torch.from_numpy(np.array(old_log_prob)).float().to(self.device)
target_value = torch.from_numpy(np.array(target_value)).float().to(self.device)
advantage = torch.from_numpy(np.array(advantage)).float().to(self.device)
for _ in range(BATCHES_PER_UPDATE):
idx = np.random.randint(0, len(transitions), BATCH_SIZE)
loss = self._calc_loss(state[idx], action[idx], old_log_prob[idx], target_value[idx], advantage[idx])
# ugly code yeah =)
# optimization outside
yield loss
def _compute_lambda_returns_and_gae(self, trajectory):
lambda_returns = []
gae = []
last_lr = 0.
last_v = 0.
for s, _, r, _ in reversed(trajectory):
ret = r + GAMMA * (last_v * (1 - LAMBDA) + last_lr * LAMBDA)
last_lr = ret
last_v = self.get_value(s)
lambda_returns.append(last_lr)
gae.append(last_lr - last_v)
# Each transition contains state, action, old action probability, value estimation and advantage estimation
return [(s, a, p, v, adv) for (s, a, _, p), v, adv in zip(trajectory, reversed(lambda_returns), reversed(gae))]
def get_value(self, state):
with torch.no_grad():
state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)
value = self.critic.get_value(state)
return value.cpu().item()
def act(self, state):
with torch.no_grad():
state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)
action, pure_action, log_prob = self.actor.act(state)
return action.cpu().numpy()[0], pure_action.cpu().numpy()[0], log_prob.cpu().item()
def save(self):
torch.save(self.actor, "agent.pkl")
class PPO():
def __init__(self, state_dim, action_dim, device):
self.state_dim = state_dim
self.action_dim = action_dim
self.device = device
self.actor = Actor(state_dim, action_dim).to(device)
self.critic = Critic(state_dim).to(device)
self.optimizer = torch.optim.Adam(
itertools.chain(self.actor.parameters(), self.critic.parameters()),
LR)
self.philosophers = list()
for i in range(P_COUNT):
self.philosophers.append(Critic(state_dim).to(device))
self.p_optimizers = [
torch.optim.Adam(p.parameters(), lr=P_LR)
for p in self.philosophers
]
self.update_cnt = 0
def _calc_loss(self, state, action, old_log_prob, expected_values, gae):
new_log_prob, action_distr = self.actor.compute_proba(state, action)
state_values = self.critic.get_value(state).squeeze(1)
critic_loss = ((expected_values - state_values)**2).mean()
unclipped_ratio = torch.exp(new_log_prob - old_log_prob)
clipped_ratio = torch.clamp(unclipped_ratio, 1 - CLIP, 1 + CLIP)
actor_loss = -torch.min(clipped_ratio * gae,
unclipped_ratio * gae).mean()
entropy_loss = -action_distr.entropy().mean()
p_loss = 0
for p in self.philosophers:
p_state_values = self.critic.get_value(state).squeeze(1)
p_loss += ((p_state_values - state_values.detach())**2).mean()
return critic_loss * VALUE_COEFF + actor_loss + entropy_loss * ENTROPY_COEF + p_loss
def update(self, trajectories):
trajectories = map(self._compute_lambda_returns_and_gae, trajectories)
transitions = sum(
trajectories,
[]) # Turn a list of trajectories into list of transitions
state, action, old_log_prob, target_value, advantage = zip(
*transitions)
state = torch.from_numpy(np.array(state)).float().to(self.device)
action = torch.from_numpy(np.array(action)).float().to(self.device)
old_log_prob = torch.from_numpy(np.array(old_log_prob)).float().to(
self.device)
target_value = torch.from_numpy(np.array(target_value)).float().to(
self.device)
advantage = torch.from_numpy(np.array(advantage)).float().to(
self.device)
for _ in range(BATCHES_PER_UPDATE):
idx = np.random.randint(0, len(transitions), BATCH_SIZE)
loss = self._calc_loss(state[idx], action[idx], old_log_prob[idx],
target_value[idx], advantage[idx])
self.optimizer.zero_grad()
for p_optimizer in self.p_optimizers:
p_optimizer.zero_grad()
loss.backward()
self.optimizer.step()
for p_optimizer in self.p_optimizers:
p_optimizer.step()
self.update_cnt += 1
if self.update_cnt % P_DELAY == 0:
self.critic = self.philosophers[0]
self.optimizer = self.p_optimizers[0]
self.philosophers.pop(0)
self.philosophers.append(Critic(self.state_dim).to(self.device))
self.p_optimizers.pop(0)
self.p_optimizers.append(
torch.optim.Adam(self.philosophers[-1].parameters(), lr=P_LR))
def _compute_lambda_returns_and_gae(self, trajectory):
lambda_returns = []
gae = []
last_lr = 0.
last_v = 0.
for s, _, r, _ in reversed(trajectory):
ret = r + GAMMA * (last_v * (1 - LAMBDA) + last_lr * LAMBDA)
last_lr = ret
last_v = self.get_value(s)
lambda_returns.append(last_lr)
gae.append(last_lr - last_v)
# Each transition contains state, action, old action probability, value estimation and advantage estimation
return [(s, a, p, v, adv) for (s, a, _, p), v, adv in zip(
trajectory, reversed(lambda_returns), reversed(gae))]
def get_value(self, state):
with torch.no_grad():
state = torch.from_numpy(state).float().unsqueeze(0).to(
self.device)
value = self.critic.get_value(state)
return value.cpu().item()
def act(self, state):
with torch.no_grad():
state = torch.from_numpy(state).float().unsqueeze(0).to(
self.device)
action, pure_action, log_prob = self.actor.act(state)
return action.cpu().numpy()[0], pure_action.cpu().numpy(
)[0], log_prob.cpu().item()
def save(self):
torch.save(self.actor, "agent.pkl")
class PPO():
def __init__(self, state_dim, action_dim):
self.actor = Actor(state_dim, action_dim)
self.critic = Critic(state_dim)
self.optimizer = torch.optim.Adam(
itertools.chain(self.actor.parameters(), self.critic.parameters()),
LR)
def _calc_loss(self, state, action, old_log_prob, expected_values, gae):
new_log_prob, action_distr = self.actor.compute_proba(state, action)
state_values = self.critic.get_value(state).squeeze(1)
critic_loss = ((expected_values - state_values)**2).mean()
unclipped_ratio = torch.exp(new_log_prob - old_log_prob)
clipped_ratio = torch.clamp(unclipped_ratio, 1 - CLIP, 1 + CLIP)
actor_loss = -torch.min(clipped_ratio * gae,
unclipped_ratio * gae).mean()
entropy_loss = -action_distr.entropy().mean()
return critic_loss * VALUE_COEFF + actor_loss + entropy_loss * ENTROPY_COEF
def update(self, trajectories):
trajectories = map(self._compute_lambda_returns_and_gae, trajectories)
transitions = sum(
trajectories,
[]) # Turn a list of trajectories into list of transitions
state, action, old_log_prob, target_value, advantage = zip(
*transitions)
state = np.array(state)
action = np.array(action)
old_log_prob = np.array(old_log_prob)
target_value = np.array(target_value)
advantage = np.array(advantage)
advnatage = (advantage - advantage.mean()) / (advantage.std() + 1e-8)
for _ in range(BATCHES_PER_UPDATE):
idx = np.random.randint(0, len(transitions),
BATCH_SIZE) # Choose random batch
s = torch.from_numpy(state[idx]).float()
a = torch.from_numpy(action[idx]).float()
op = torch.from_numpy(old_log_prob[idx]).float(
) # Log probability of the action in state s.t. old policy
v = torch.from_numpy(
target_value[idx]).float() # Estimated by lambda-returns
adv = torch.from_numpy(advantage[idx]).float(
) # Estimated by generalized advantage estimation
loss = self._calc_loss(s, a, op, v, adv)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
def _compute_lambda_returns_and_gae(self, trajectory):
lambda_returns = []
gae = []
last_lr = 0.
last_v = 0.
for s, _, r, _ in reversed(trajectory):
ret = r + GAMMA * (last_v * (1 - LAMBDA) + last_lr * LAMBDA)
last_lr = ret
last_v = self.get_value(s)
lambda_returns.append(last_lr)
gae.append(last_lr - last_v)
# Each transition contains state, action, old action probability, value estimation and advantage estimation
return [(s, a, p, v, adv) for (s, a, _, p), v, adv in zip(
trajectory, reversed(lambda_returns), reversed(gae))]
def get_value(self, state):
with torch.no_grad():
state = torch.from_numpy(state).float().unsqueeze(0)
value = self.critic.get_value(state)
return value.cpu().item()
def act(self, state):
with torch.no_grad():
state = torch.from_numpy(state).float().unsqueeze(0)
action, pure_action, log_prob = self.actor.act(state)
return action.cpu().numpy()[0], pure_action.cpu().numpy(
)[0], log_prob.cpu().item()
def save(self):
torch.save(self.actor, "agent.pkl")
