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33 lines
1.2 KiB
Python
33 lines
1.2 KiB
Python
from algorithms.marl.base_ac import BaseActorCritic
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from algorithms.marl.base_ac import nms
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import torch
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from torch.distributions import Categorical
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from pathlib import Path
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class LoopSNAC(BaseActorCritic):
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def __init__(self, cfg):
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super().__init__(cfg)
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def load_state_dict(self, path: Path):
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path2weights = list(path.glob('*.pt'))
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assert len(path2weights) == 1, f'Expected a single set of weights but got {len(path2weights)}'
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self.net.load_state_dict(torch.load(path2weights[0]))
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def init_hidden(self):
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hidden_actor = self.net.init_hidden_actor()
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hidden_critic = self.net.init_hidden_critic()
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return dict(hidden_actor=torch.cat([hidden_actor] * self.n_agents, 0),
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hidden_critic=torch.cat([hidden_critic] * self.n_agents, 0)
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)
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def get_actions(self, out):
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actions = Categorical(logits=out[nms.LOGITS]).sample().squeeze()
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return actions
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def forward(self, observations, actions, hidden_actor, hidden_critic):
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out = self.net(self._as_torch(observations).unsqueeze(1),
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self._as_torch(actions).unsqueeze(1),
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hidden_actor, hidden_critic
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)
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return out |