43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
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import torch
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import torch.nn.functional as F
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from tianshou.data import Batch
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from tianshou.policy import PGPolicy
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class A2CPolicy(PGPolicy):
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"""docstring for A2CPolicy"""
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def __init__(self, model, optim, dist_fn=torch.distributions.Categorical,
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discount_factor=0.99, vf_coef=.5, entropy_coef=.01):
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super().__init__(model, optim, dist_fn, discount_factor)
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self._w_value = vf_coef
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self._w_entropy = entropy_coef
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def __call__(self, batch, state=None):
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logits, value, h = self.model(batch.obs, state=state, info=batch.info)
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logits = F.softmax(logits, dim=1)
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dist = self.dist_fn(logits)
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act = dist.sample().detach().cpu().numpy()
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return Batch(logits=logits, act=act, state=h, dist=dist, value=value)
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def learn(self, batch, batch_size=None):
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losses = []
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for b in batch.split(batch_size):
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self.optim.zero_grad()
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result = self(b)
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dist = result.dist
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v = result.value
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a = torch.tensor(b.act, device=dist.logits.device)
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r = torch.tensor(b.returns, device=dist.logits.device)
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actor_loss = -(dist.log_prob(a) * (r - v).detach()).mean()
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critic_loss = (r - v).pow(2).mean()
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entropy_loss = dist.entropy().mean()
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loss = actor_loss \
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+ self._w_value * critic_loss \
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- self._w_entropy * entropy_loss
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loss.backward()
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self.optim.step()
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losses.append(loss.detach().cpu().numpy())
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return losses
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