from operator import itemgetter
import numpy as np
import torch
from argparse import Namespace
from typing import List, Optional
from torch import nn, Tensor
from xuance.torch.learners.multi_agent_rl.commnet_learner import CommNet_Learner
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class IC3Net_Learner(CommNet_Learner):
def __init__(self,
config: Namespace,
model_keys: List[str],
agent_keys: List[str],
policy: nn.Module,
callback):
super(IC3Net_Learner, self).__init__(config, model_keys, agent_keys, policy, callback)
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def build_training_data(self, sample: Optional[dict],
use_parameter_sharing: Optional[bool] = False,
use_actions_mask: Optional[bool] = False,
use_global_state: Optional[bool] = False):
batch_size = sample['batch_size']
seq_length = sample['sequence_length'] if self.use_rnn else 1
state, avail_actions, filled, IDs = None, None, None, None
if use_parameter_sharing:
k = self.model_keys[0]
bs = batch_size * self.n_agents
obs_tensor = Tensor(np.stack(itemgetter(*self.agent_keys)(sample['obs']), axis=1)).to(self.device)
actions_tensor = Tensor(np.stack(itemgetter(*self.agent_keys)(sample['actions']), axis=1)).to(self.device)
values_tensor = Tensor(np.stack(itemgetter(*self.agent_keys)(sample['values']), axis=1)).to(self.device)
returns_tensor = Tensor(np.stack(itemgetter(*self.agent_keys)(sample['returns']), axis=1)).to(self.device)
advantages_tensor = Tensor(np.stack(itemgetter(*self.agent_keys)(sample['advantages']), 1)).to(self.device)
log_pi_old_tensor = Tensor(np.stack(itemgetter(*self.agent_keys)(sample['log_pi_old']), 1)).to(self.device)
log_pi_gate_old = Tensor(np.stack(itemgetter(*self.agent_keys)(sample['gate_log_pi_old']), 1)).to(self.device)
ter_tensor = Tensor(np.stack(itemgetter(*self.agent_keys)(sample['terminals']), 1)).float().to(self.device)
msk_tensor = Tensor(np.stack(itemgetter(*self.agent_keys)(sample['agent_mask']), 1)).float().to(self.device)
if self.use_rnn:
obs = {k: obs_tensor.reshape(bs, seq_length, -1)}
if len(actions_tensor.shape) == 3:
actions = {k: actions_tensor.reshape(bs, seq_length)}
elif len(actions_tensor.shape) == 4:
actions = {k: actions_tensor.reshape(bs, seq_length, -1)}
else:
raise AttributeError("Wrong actions shape.")
# merge batch_size and agents
values = {k: values_tensor.reshape(bs, seq_length)}
returns = {k: returns_tensor.reshape(bs, seq_length)}
advantages = {k: advantages_tensor.reshape(bs, seq_length)}
log_pi_old = {k: log_pi_old_tensor.reshape(bs, seq_length)}
log_pi_gate_old = {k: log_pi_gate_old.reshape(bs, seq_length)}
terminals = {k: ter_tensor.reshape(bs, seq_length)}
agent_mask = {k: msk_tensor.reshape(bs, seq_length)}
IDs = torch.eye(self.n_agents).unsqueeze(1).unsqueeze(0).expand(
batch_size, -1, seq_length, -1).reshape(bs, seq_length, self.n_agents).to(self.device)
else:
obs = {k: obs_tensor.reshape(bs, -1)}
if len(actions_tensor.shape) == 2:
actions = {k: actions_tensor.reshape(bs)}
elif len(actions_tensor.shape) == 3:
actions = {k: actions_tensor.reshape(bs, -1)}
else:
raise AttributeError("Wrong actions shape.")
values = {k: values_tensor.reshape(bs)}
returns = {k: returns_tensor.reshape(bs)}
advantages = {k: advantages_tensor.reshape(bs)}
log_pi_old = {k: log_pi_old_tensor.reshape(bs)}
terminals = {k: ter_tensor.reshape(bs)}
agent_mask = {k: msk_tensor.reshape(bs)}
IDs = torch.eye(self.n_agents).unsqueeze(0).expand(
batch_size, -1, -1).reshape(bs, self.n_agents).to(self.device)
if use_actions_mask:
avail_a = np.stack(itemgetter(*self.agent_keys)(sample['avail_actions']), axis=1)
if self.use_rnn:
avail_actions = {k: Tensor(avail_a.reshape([bs, seq_length, -1])).float().to(self.device)}
else:
avail_actions = {k: Tensor(avail_a.reshape([bs, -1])).float().to(self.device)}
else:
obs = {k: Tensor(sample['obs'][k]).to(self.device) for k in self.agent_keys}
actions = {k: Tensor(sample['actions'][k]).to(self.device) for k in self.agent_keys}
values = {k: Tensor(sample['values'][k]).to(self.device) for k in self.agent_keys}
returns = {k: Tensor(sample['returns'][k]).to(self.device) for k in self.agent_keys}
advantages = {k: Tensor(sample['advantages'][k]).to(self.device) for k in self.agent_keys}
log_pi_old = {k: Tensor(sample['log_pi_old'][k]).to(self.device) for k in self.agent_keys}
log_pi_gate_old = {k: Tensor(sample['gate_log_pi_old'][k]).to(self.device) for k in self.agent_keys}
terminals = {k: Tensor(sample['terminals'][k]).float().to(self.device) for k in self.agent_keys}
agent_mask = {k: Tensor(sample['agent_mask'][k]).float().to(self.device) for k in self.agent_keys}
if use_actions_mask:
avail_actions = {k: Tensor(sample['avail_actions'][k]).float().to(self.device) for k in self.agent_keys}
if use_global_state:
state = Tensor(sample['state']).to(self.device)
if self.use_rnn:
filled = Tensor(sample['filled']).float().to(self.device)
sample_Tensor = {
'batch_size': batch_size,
'state': state,
'obs': obs,
'actions': actions,
'values': values,
'returns': returns,
'advantages': advantages,
'log_pi_old': log_pi_old,
'log_pi_gate_old': log_pi_gate_old,
'terminals': terminals,
'agent_mask': agent_mask,
'avail_actions': avail_actions,
'agent_ids': IDs,
'filled': filled,
'seq_length': seq_length,
}
return sample_Tensor
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def update_rnn(self, sample):
self.iterations += 1
info = {}
sample_Tensor = self.build_training_data(sample=sample,
use_parameter_sharing=self.use_parameter_sharing,
use_actions_mask=self.use_actions_mask)
batch_size = sample_Tensor['batch_size']
bs_rnn = batch_size * self.n_agents if self.use_parameter_sharing else batch_size
obs = sample_Tensor['obs']
actions = sample_Tensor['actions']
values = sample_Tensor['values']
returns = sample_Tensor['returns']
advantages = sample_Tensor['advantages']
log_pi_old = sample_Tensor['log_pi_old']
log_pi_gate_old = sample_Tensor['log_pi_gate_old']
avail_actions = sample_Tensor['avail_actions']
agent_mask = sample_Tensor['agent_mask']
filled = sample_Tensor['filled']
seq_len = filled.shape[1]
IDs = sample_Tensor['agent_ids']
if self.use_parameter_sharing:
key = self.model_keys[0]
# agent_mask: [batch_size*self.n_agents, seq_length]
alive_ally = agent_mask[key].view(batch_size, self.n_agents, seq_len).unsqueeze(-1)
alive_ally = {k: alive_ally[:, i] for i, k in enumerate(self.agent_keys)}
else:
alive_ally = {k: agent_mask[k].unsqueeze(-1) for k in self.model_keys}
if self.use_parameter_sharing:
filled = filled.unsqueeze(1).expand(batch_size, self.n_agents, seq_len).reshape(bs_rnn, seq_len)
# feedfowrd
rnn_hidden_actor = {k: self.policy.actor_representation[k].init_hidden(bs_rnn) for k in self.model_keys}
rnn_hidden_critic = {k: self.policy.critic_representation[k].init_hidden(bs_rnn) for k in self.model_keys}
# feedforward
_, pi_dist_dict, gate_log_probs = self.policy(obs, agent_ids=IDs, avail_actions=avail_actions, rnn_hidden=rnn_hidden_actor, alive_ally=alive_ally)
_, value_pred_dict = self.policy.get_values(observation=obs, agent_ids=IDs, rnn_hidden=rnn_hidden_critic, alive_ally=alive_ally)
# calculate losses for each agent
loss_gate, loss_a, loss_e, loss_c = [], [], [], []
for key in self.model_keys:
# gate_loss
mask_values = agent_mask[key] * filled
log_pi_gate = gate_log_probs[key].reshape(bs_rnn, seq_len)
ratio = torch.exp(log_pi_gate - log_pi_gate_old[key])
surrogate1 = ratio * advantages[key]
surrogate2 = torch.clip(ratio, 1 - self.clip_range, 1 + self.clip_range) * advantages[key]
loss_gate.append(-(torch.min(surrogate1, surrogate2) * mask_values).sum() / mask_values.sum())
# actor_loss
mask_values = agent_mask[key] * filled
log_pi = pi_dist_dict[key].log_prob(actions[key]).reshape(bs_rnn, seq_len)
ratio = torch.exp(log_pi - log_pi_old[key])
surrogate1 = ratio * advantages[key]
surrogate2 = torch.clip(ratio, 1 - self.clip_range, 1 + self.clip_range) * advantages[key]
loss_a.append(-(torch.min(surrogate1, surrogate2) * mask_values).sum() / mask_values.sum())
# entropy loss
entropy = pi_dist_dict[key].entropy().reshape(bs_rnn, seq_len)
entropy = entropy * mask_values
loss_e.append(entropy.sum() / mask_values.sum())
# critic loss
value_pred_i = value_pred_dict[key].reshape(bs_rnn, seq_len)
value_target = returns[key].reshape(bs_rnn, seq_len)
values_i = values[key].reshape(bs_rnn, seq_len)
if self.use_value_clip:
value_clipped = values_i + (value_pred_i - values_i).clamp(-self.value_clip_range,
self.value_clip_range)
if self.use_value_norm:
self.value_normalizer[key].update(value_target.reshape(-1, 1))
value_target = self.value_normalizer[key].normalize(value_target.reshape(-1, 1))
value_target = value_target.reshape(bs_rnn, seq_len)
if self.use_huber_loss:
loss_v = self.huber_loss(value_pred_i, value_target)
loss_v_clipped = self.huber_loss(value_clipped, value_target)
else:
loss_v = (value_pred_i - value_target) ** 2
loss_v_clipped = (value_clipped - value_target) ** 2
loss_c_ = torch.max(loss_v, loss_v_clipped) * mask_values
loss_c.append(loss_c_.sum() / mask_values.sum())
else:
if self.use_value_norm:
self.value_normalizer[key].update(value_target)
value_target = self.value_normalizer[key].normalize(value_target)
if self.use_huber_loss:
loss_v = self.huber_loss(value_pred_i, value_target)
else:
loss_v = (value_pred_i - value_target) ** 2
loss_c.append((loss_v * mask_values).sum() / mask_values.sum())
info.update({
f"{key}/actor_loss": loss_a[-1].item(),
f"{key}/critic_loss": loss_c[-1].item(),
f"{key}/entropy": loss_e[-1].item(),
f"{key}/predict_value": value_pred_i.mean().item()
})
loss = sum(loss_gate) + sum(loss_a) + self.vf_coef * sum(loss_c) - self.ent_coef * sum(loss_e)
self.optimizer.zero_grad()
loss.backward()
if self.use_grad_clip:
grad_norm = torch.nn.utils.clip_grad_norm_(self.policy.parameters_model, self.grad_clip_norm)
info["gradient_norm"] = grad_norm.item()
self.optimizer.step()
if self.scheduler is not None:
self.scheduler.step()
# Logger
lr = self.optimizer.state_dict()['param_groups'][0]['lr']
info.update({
"learning_rate": lr,
"loss": loss.item(),
})
return info