60cfc373f8
* Add auto alpha tuning and exploration noise for sac. Add class BaseNoise and GaussianNoise for the concept of exploration noise. Add new test for sac tested in MountainCarContinuous-v0, which should benefits from the two above new feature. * add exploration noise to collector, fix example to adapt modification * fix #98 * enable off-policy to update multiple times in one step. (#99)
82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
import torch
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import numpy as np
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from torch import nn
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class Actor(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape,
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max_action, device='cpu'):
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super().__init__()
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self.device = device
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self.model = [
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nn.Linear(np.prod(state_shape), 128),
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nn.ReLU(inplace=True)]
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for i in range(layer_num):
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self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
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self.model += [nn.Linear(128, np.prod(action_shape))]
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self.model = nn.Sequential(*self.model)
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self._max = max_action
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def forward(self, s, **kwargs):
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s = torch.tensor(s, device=self.device, dtype=torch.float)
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batch = s.shape[0]
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s = s.view(batch, -1)
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logits = self.model(s)
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logits = self._max * torch.tanh(logits)
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return logits, None
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class ActorProb(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape,
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max_action, device='cpu', unbounded=False):
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super().__init__()
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self.device = device
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self.model = [
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nn.Linear(np.prod(state_shape), 128),
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nn.ReLU(inplace=True)]
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for i in range(layer_num):
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self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
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self.model = nn.Sequential(*self.model)
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self.mu = nn.Linear(128, np.prod(action_shape))
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self.sigma = nn.Linear(128, np.prod(action_shape))
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self._max = max_action
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self._unbounded = unbounded
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def forward(self, s, **kwargs):
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if not isinstance(s, torch.Tensor):
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s = torch.tensor(s, device=self.device, dtype=torch.float)
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batch = s.shape[0]
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s = s.view(batch, -1)
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logits = self.model(s)
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if not self._unbounded:
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mu = self._max * torch.tanh(self.mu(logits))
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sigma = torch.exp(self.sigma(logits))
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return (mu, sigma), None
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class Critic(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape=0, device='cpu'):
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super().__init__()
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self.device = device
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self.model = [
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nn.Linear(np.prod(state_shape) + np.prod(action_shape), 128),
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nn.ReLU(inplace=True)]
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for i in range(layer_num):
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self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
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self.model += [nn.Linear(128, 1)]
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self.model = nn.Sequential(*self.model)
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def forward(self, s, a=None):
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if not isinstance(s, torch.Tensor):
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s = torch.tensor(s, device=self.device, dtype=torch.float)
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if a is not None and not isinstance(a, torch.Tensor):
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a = torch.tensor(a, device=self.device, dtype=torch.float)
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batch = s.shape[0]
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s = s.view(batch, -1)
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if a is None:
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logits = self.model(s)
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else:
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a = a.view(batch, -1)
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logits = self.model(torch.cat([s, a], dim=1))
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return logits
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