"""
Independent Q-learning (IQL)
Implementation: Pytorch
"""
import torch
from torch import nn
from xuance.torch.learners import LearnerMAS
from xuance.common import List
from argparse import Namespace
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class IQL_Learner(LearnerMAS):
def __init__(self,
config: Namespace,
model_keys: List[str],
agent_keys: List[str],
policy: nn.Module,
callback):
super(IQL_Learner, self).__init__(config, model_keys, agent_keys, policy, callback)
self.optimizer = {key: torch.optim.Adam(self.policy.parameters_model[key], config.learning_rate, eps=1e-5)
for key in self.model_keys}
self.scheduler = {key: torch.optim.lr_scheduler.LinearLR(self.optimizer[key],
start_factor=1.0,
end_factor=self.end_factor_lr_decay,
total_iters=self.total_iters)
for key in self.model_keys}
self.gamma = config.gamma
self.sync_frequency = config.sync_frequency
self.n_actions = {k: self.policy.action_space[k].n for k in self.model_keys}
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def update(self, sample):
self.iterations += 1
# prepare training data
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']
obs = sample_Tensor['obs']
actions = sample_Tensor['actions']
obs_next = sample_Tensor['obs_next']
rewards = sample_Tensor['rewards']
terminals = sample_Tensor['terminals']
agent_mask = sample_Tensor['agent_mask']
avail_actions = sample_Tensor['avail_actions']
avail_actions_next = sample_Tensor['avail_actions_next']
IDs = sample_Tensor['agent_ids']
if self.use_parameter_sharing:
key = self.model_keys[0]
bs = batch_size * self.n_agents
rewards[key] = rewards[key].reshape(batch_size * self.n_agents)
terminals[key] = terminals[key].reshape(batch_size * self.n_agents)
else:
bs = batch_size
info = self.callback.on_update_start(self.iterations, method="update",
policy=self.policy, sample_Tensor=sample_Tensor, bs=bs)
_, _, q_eval = self.policy(observation=obs, agent_ids=IDs, avail_actions=avail_actions)
_, q_next = self.policy.Qtarget(observation=obs_next, agent_ids=IDs)
for key in self.model_keys:
mask_values = agent_mask[key]
q_eval_a = q_eval[key].gather(-1, actions[key].long().unsqueeze(-1)).reshape(bs)
if self.use_actions_mask:
q_next[key][avail_actions_next[key] == 0] = -1e10
if self.config.double_q:
_, actions_next_greedy, _ = self.policy(obs_next, IDs, agent_key=key, avail_actions=avail_actions)
q_next_a = q_next[key].gather(-1, actions_next_greedy[key].unsqueeze(-1).long()).reshape(bs)
else:
q_next_a = q_next[key].max(dim=-1, keepdim=True).values.reshape(bs)
q_target = rewards[key] + (1 - terminals[key]) * self.gamma * q_next_a
# calculate the loss function
td_error = (q_eval_a - q_target.detach()) * mask_values
loss = (td_error ** 2).sum() / mask_values.sum()
self.optimizer[key].zero_grad()
loss.backward()
if self.use_grad_clip:
torch.nn.utils.clip_grad_norm_(self.policy.parameters_model[key], self.grad_clip_norm)
self.optimizer[key].step()
if self.scheduler[key] is not None:
self.scheduler[key].step()
lr = self.optimizer[key].state_dict()['param_groups'][0]['lr']
info.update({
f"{key}/learning_rate": lr,
f"{key}/loss_Q": loss.item(),
f"{key}/predictQ": q_eval_a.mean().item()
})
info.update(self.callback.on_update_agent_wise(self.iterations, key, info=info, method="update",
mask_values=mask_values, q_eval_a=q_eval_a,
q_next_a=q_next_a, q_target=q_target,
td_error=td_error, loss=loss))
if self.iterations % self.sync_frequency == 0:
self.policy.copy_target()
info.update(self.callback.on_update_end(self.iterations, method="update", policy=self.policy, info=info))
return info
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def update_rnn(self, sample):
self.iterations += 1
# prepare training data
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']
seq_len = sample_Tensor['seq_length']
obs = sample_Tensor['obs']
actions = sample_Tensor['actions']
rewards = sample_Tensor['rewards']
terminals = sample_Tensor['terminals']
agent_mask = sample_Tensor['agent_mask']
avail_actions = sample_Tensor['avail_actions']
filled = sample_Tensor['filled']
IDs = sample_Tensor['agent_ids']
if self.use_parameter_sharing:
key = self.model_keys[0]
bs_rnn = batch_size * self.n_agents
filled = filled.unsqueeze(1).expand(-1, self.n_agents, -1).reshape(bs_rnn, seq_len)
rewards[key] = rewards[key].reshape(batch_size * self.n_agents, seq_len)
terminals[key] = terminals[key].reshape(batch_size * self.n_agents, seq_len)
else:
bs_rnn = batch_size
info = self.callback.on_update_start(self.iterations, method="update_rnn",
policy=self.policy, sample_Tensor=sample_Tensor, bs_rnn=bs_rnn)
rnn_hidden = {k: self.policy.representation[k].init_hidden(bs_rnn) for k in self.model_keys}
_, actions_greedy, q_eval = self.policy(observation=obs, agent_ids=IDs, avail_actions=avail_actions,
rnn_hidden=rnn_hidden)
target_rnn_hidden = {k: self.policy.target_representation[k].init_hidden(bs_rnn) for k in self.model_keys}
_, q_next_seq = self.policy.Qtarget(observation=obs, agent_ids=IDs, rnn_hidden=target_rnn_hidden)
for key in self.model_keys:
mask_values = agent_mask[key] * filled
# calculate the target Q values
q_eval_a = q_eval[key][:, :-1].gather(-1, actions[key].long().unsqueeze(-1)).reshape(bs_rnn, seq_len)
q_next = q_next_seq[key][:, 1:]
if self.use_actions_mask:
q_next[avail_actions[key][:, 1:] == 0] = -1e10
if self.config.double_q:
actions_next_greedy = actions_greedy[key][:, 1:].unsqueeze(-1)
q_next_a = q_next.gather(-1, actions_next_greedy.long().detach()).reshape(bs_rnn, seq_len)
else:
q_next_a = q_next.max(dim=-1, keepdim=True).values.reshape(bs_rnn, seq_len)
q_target = rewards[key] + (1 - terminals[key]) * self.gamma * q_next_a
# calculate the loss function
td_errors = (q_eval_a - q_target.detach()) * mask_values
loss = (td_errors ** 2).sum() / mask_values.sum()
self.optimizer[key].zero_grad()
loss.backward()
if self.use_grad_clip:
torch.nn.utils.clip_grad_norm_(self.policy.parameters_model[key], self.grad_clip_norm)
self.optimizer[key].step()
if self.scheduler is not None:
self.scheduler[key].step()
lr = self.optimizer[key].state_dict()['param_groups'][0]['lr']
info.update({
f"{key}/learning_rate": lr,
f"{key}/loss_Q": loss.item(),
f"{key}/predictQ": q_eval_a.mean().item()
})
info.update(self.callback.on_update_agent_wise(self.iterations, key, info=info, method="update_rnn",
mask_values=mask_values, q_eval_a=q_eval_a,
q_next_a=q_next_a, q_target=q_target,
td_error=td_errors, loss=loss))
if self.iterations % self.sync_frequency == 0:
self.policy.copy_target()
info.update(self.callback.on_update_end(self.iterations, method="update_rnn", policy=self.policy, info=info))
return info