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happo_trainer.py
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import numpy as np
import torch
import torch.nn as nn
from bidexhands.utils.util import get_gard_norm, huber_loss, mse_loss
from bidexhands.algorithms.marl.utils.popart import PopArt
from bidexhands.algorithms.marl.actor_critic import Actor, Critic
from bidexhands.algorithms.utils.util import check
class HAPPO():
"""
Trainer class for HAPPO to update policies.
:param args: (argparse.Namespace) arguments containing relevant model, policy, and env information.
:param policy: (HAPPO_Policy) policy to update.
:param device: (torch.device) specifies the device to run on (cpu/gpu).
"""
def __init__(self,
config,
policy,
device=torch.device("cpu")):
self.device = device
self.tpdv = dict(dtype=torch.float32, device=device)
self.policy = policy
self.clip_param = config["clip_param"]
self.ppo_epoch = config["ppo_epoch"]
self.num_mini_batch = config["num_mini_batch"]
self.data_chunk_length = config["data_chunk_length"]
self.value_loss_coef = config["value_loss_coef"]
self.entropy_coef = config["entropy_coef"]
self.max_grad_norm = config["max_grad_norm"]
self.huber_delta = config["huber_delta"]
self._use_recurrent_policy = config["use_recurrent_policy"]
self._use_naive_recurrent = config["use_naive_recurrent_policy"]
self._use_max_grad_norm = config["use_max_grad_norm"]
self._use_clipped_value_loss = config["use_clipped_value_loss"]
self._use_huber_loss = config["use_huber_loss"]
self._use_popart = config["use_popart"]
self._use_value_active_masks = config["use_value_active_masks"]
self._use_policy_active_masks = config["use_policy_active_masks"]
if self._use_popart:
self.value_normalizer = PopArt(1, device=self.device)
else:
self.value_normalizer = None
def cal_value_loss(self, values, value_preds_batch, return_batch, active_masks_batch):
"""
Calculate value function loss.
:param values: (torch.Tensor) value function predictions.
:param value_preds_batch: (torch.Tensor) "old" value predictions from data batch (used for value clip loss)
:param return_batch: (torch.Tensor) reward to go returns.
:param active_masks_batch: (torch.Tensor) denotes if agent is active or dead at a given timesep.
:return value_loss: (torch.Tensor) value function loss.
"""
if self._use_popart:
value_pred_clipped = value_preds_batch + (values - value_preds_batch).clamp(-self.clip_param,
self.clip_param)
error_clipped = self.value_normalizer(return_batch) - value_pred_clipped
error_original = self.value_normalizer(return_batch) - values
else:
value_pred_clipped = value_preds_batch + (values - value_preds_batch).clamp(-self.clip_param,
self.clip_param)
error_clipped = return_batch - value_pred_clipped
error_original = return_batch - values
if self._use_huber_loss:
value_loss_clipped = huber_loss(error_clipped, self.huber_delta)
value_loss_original = huber_loss(error_original, self.huber_delta)
else:
value_loss_clipped = mse_loss(error_clipped)
value_loss_original = mse_loss(error_original)
if self._use_clipped_value_loss:
value_loss = torch.max(value_loss_original, value_loss_clipped)
else:
value_loss = value_loss_original
if self._use_value_active_masks:
value_loss = (value_loss * active_masks_batch).sum() / active_masks_batch.sum()
else:
value_loss = value_loss.mean()
return value_loss
def ppo_update(self, sample, update_actor=True):
"""
Update actor and critic networks.
:param sample: (Tuple) contains data batch with which to update networks.
:update_actor: (bool) whether to update actor network.
:return value_loss: (torch.Tensor) value function loss.
:return critic_grad_norm: (torch.Tensor) gradient norm from critic update.
;return policy_loss: (torch.Tensor) actor(policy) loss value.
:return dist_entropy: (torch.Tensor) action entropies.
:return actor_grad_norm: (torch.Tensor) gradient norm from actor update.
:return imp_weights: (torch.Tensor) importance sampling weights.
"""
share_obs_batch, obs_batch, rnn_states_batch, rnn_states_critic_batch, actions_batch, \
value_preds_batch, return_batch, masks_batch, active_masks_batch, old_action_log_probs_batch, \
adv_targ, available_actions_batch, factor_batch = sample
old_action_log_probs_batch = check(old_action_log_probs_batch).to(**self.tpdv)
adv_targ = check(adv_targ).to(**self.tpdv)
value_preds_batch = check(value_preds_batch).to(**self.tpdv)
return_batch = check(return_batch).to(**self.tpdv)
active_masks_batch = check(active_masks_batch).to(**self.tpdv)
factor_batch = check(factor_batch).to(**self.tpdv)
# Reshape to do in a single forward pass for all steps
values, action_log_probs, dist_entropy = self.policy.evaluate_actions(share_obs_batch,
obs_batch,
rnn_states_batch,
rnn_states_critic_batch,
actions_batch,
masks_batch,
available_actions_batch,
active_masks_batch)
# actor update
imp_weights = torch.exp((action_log_probs - old_action_log_probs_batch).sum(dim=-1, keepdim=True))
surr1 = imp_weights * adv_targ
surr2 = torch.clamp(imp_weights, 1.0 - self.clip_param, 1.0 + self.clip_param) * adv_targ
if self._use_policy_active_masks:
policy_action_loss = (-torch.sum(factor_batch * torch.min(surr1, surr2),
dim=-1,
keepdim=True) * active_masks_batch).sum() / active_masks_batch.sum()
else:
policy_action_loss = -torch.sum(factor_batch * torch.min(surr1, surr2), dim=-1, keepdim=True).mean()
policy_loss = policy_action_loss
self.policy.actor_optimizer.zero_grad()
if update_actor:
(policy_loss - dist_entropy * self.entropy_coef).backward()
if self._use_max_grad_norm:
actor_grad_norm = nn.utils.clip_grad_norm_(self.policy.actor.parameters(), self.max_grad_norm)
else:
actor_grad_norm = get_gard_norm(self.policy.actor.parameters())
self.policy.actor_optimizer.step()
value_loss = self.cal_value_loss(values, value_preds_batch, return_batch, active_masks_batch)
self.policy.critic_optimizer.zero_grad()
(value_loss * self.value_loss_coef).backward()
if self._use_max_grad_norm:
critic_grad_norm = nn.utils.clip_grad_norm_(self.policy.critic.parameters(), self.max_grad_norm)
else:
critic_grad_norm = get_gard_norm(self.policy.critic.parameters())
self.policy.critic_optimizer.step()
return value_loss, critic_grad_norm, policy_loss, dist_entropy, actor_grad_norm, imp_weights
def train(self, buffer, update_actor=True):
"""
Perform a training update using minibatch GD.
:param buffer: (SharedReplayBuffer) buffer containing training data.
:param update_actor: (bool) whether to update actor network.
:return train_info: (dict) contains information regarding training update (e.g. loss, grad norms, etc).
"""
if self._use_popart:
advantages = buffer.returns[:-1] - self.value_normalizer.denormalize(buffer.value_preds[:-1])
else:
advantages = buffer.returns[:-1] - buffer.value_preds[:-1]
advantages_copy = advantages.clone()
# advantages_copy[buffer.active_masks[:-1] == 0.0] = torch.nan
mean_advantages = torch.mean(advantages_copy)
std_advantages = torch.std(advantages_copy)
advantages = (advantages - mean_advantages) / (std_advantages + 1e-5)
train_info = {}
train_info['value_loss'] = 0
train_info['policy_loss'] = 0
train_info['dist_entropy'] = 0
train_info['actor_grad_norm'] = 0
train_info['critic_grad_norm'] = 0
train_info['ratio'] = 0
for _ in range(self.ppo_epoch):
if self._use_recurrent_policy:
data_generator = buffer.recurrent_generator(advantages, self.num_mini_batch, self.data_chunk_length)
elif self._use_naive_recurrent:
data_generator = buffer.naive_recurrent_generator(advantages, self.num_mini_batch)
else:
data_generator = buffer.feed_forward_generator(advantages, self.num_mini_batch)
for sample in data_generator:
value_loss, critic_grad_norm, policy_loss, dist_entropy, actor_grad_norm, imp_weights = self.ppo_update(sample, update_actor=update_actor)
train_info['value_loss'] += value_loss.item()
train_info['policy_loss'] += policy_loss.item()
train_info['dist_entropy'] += dist_entropy.item()
train_info['actor_grad_norm'] += actor_grad_norm
train_info['critic_grad_norm'] += critic_grad_norm
train_info['ratio'] += imp_weights.mean()
num_updates = self.ppo_epoch * self.num_mini_batch
for k in train_info.keys():
train_info[k] /= num_updates
return train_info
def prep_training(self):
self.policy.actor.train()
self.policy.critic.train()
def prep_rollout(self):
self.policy.actor.eval()
self.policy.critic.eval()