mirror of
https://github.com/wassname/pytorch-soft-actor-critic.git
synced 2026-06-27 18:06:10 +08:00
Pranjal Tandon
29 lines
965 B
Python
29 lines
965 B
Python
import math
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import torch
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def create_log_gaussian(mean, log_std, t):
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quadratic = -((0.5 * (t - mean) / (log_std.exp())).pow(2))
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l = mean.shape
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log_z = log_std
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z = l[-1] * math.log(2 * math.pi)
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log_p = quadratic.sum(dim=-1) - log_z.sum(dim=-1) - 0.5 * z
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return log_p
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def logsumexp(inputs, dim=None, keepdim=False):
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if dim is None:
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inputs = inputs.view(-1)
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dim = 0
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s, _ = torch.max(inputs, dim=dim, keepdim=True)
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outputs = s + (inputs - s).exp().sum(dim=dim, keepdim=True).log()
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if not keepdim:
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outputs = outputs.squeeze(dim)
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return outputs
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def soft_update(target, source, tau):
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for target_param, param in zip(target.parameters(), source.parameters()):
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target_param.data.copy_(target_param.data * (1.0 - tau) + param.data * tau)
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def hard_update(target, source):
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for target_param, param in zip(target.parameters(), source.parameters()):
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target_param.data.copy_(param.data)
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