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Remove unused wrapper

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Shangtong Zhang
2018-05-17 23:37:50 -06:00
parent 84f7911bb4
commit f6d0c4d260
1 changed files with 0 additions and 89 deletions
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deep_rl/network/network_utils.py
-89
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@@ -23,95 +23,6 @@ class BaseNet:
x = torch.tensor(x, device=self.device, dtype=torch.float32)
return x
# class DisjointActorCriticWrapper:
# def __init__(self, state_dim, action_dim, actor_network_fn, critic_network_fn):
# self.actor = actor_network_fn(state_dim, action_dim)
# self.critic = critic_network_fn(state_dim, action_dim)
#
# def state_dict(self):
# return [self.actor.state_dict(), self.critic.state_dict()]
#
# def load_state_dict(self, state_dicts):
# self.actor.load_state_dict(state_dicts[0])
# self.critic.load_state_dict(state_dicts[1])
#
# def parameters(self):
# return list(self.actor.parameters()) + list(self.critic.parameters())
#
# def zero_grad(self):
# self.actor.zero_grad()
# self.critic.zero_grad()
#
# class GaussianActorCriticWrapper:
# def __init__(self, state_dim, action_dim, actor_fn, critic_fn, actor_opt_fn, critic_opt_fn):
# self.actor = actor_fn(state_dim, action_dim)
# self.critic = critic_fn(state_dim)
# self.actor_opt = actor_opt_fn(self.actor.parameters())
# self.critic_opt = critic_opt_fn(self.critic.parameters())
#
# def predict(self, state, actions=None):
# mean, std, log_std = self.actor.predict(state)
# values = self.critic.predict(state)
# dist = torch.distributions.Normal(mean, std)
# if actions is None:
# actions = dist.sample()
# log_probs = dist.log_prob(actions)
# log_probs = torch.sum(log_probs, dim=1, keepdim=True)
# return actions, log_probs, 0, values
#
# def tensor(self, x):
# return self.actor.tensor(x)
#
# def zero_grad(self):
# self.actor_opt.zero_grad()
# self.critic_opt.zero_grad()
#
# def parameters(self):
# return list(self.actor.parameters()) + list(self.critic.parameters())
#
# def step(self):
# self.actor_opt.step()
# self.critic_opt.step()
#
# def state_dict(self):
# return [self.actor.state_dict(), self.critic.state_dict()]
#
# def load_state_dict(self, state_dicts):
# self.actor.load_state_dict(state_dicts[0])
# self.critic.load_state_dict(state_dicts[1])
#
# class CategoricalActorCriticWrapper:
# def __init__(self, state_dim, action_dim, network_fn, opt_fn):
# self.network = network_fn(state_dim, action_dim)
# self.opt = opt_fn(self.network.parameters())
#
# def predict(self, state, action=None):
# prob, log_prob, value = self.network.predict(state)
# entropy_loss = torch.sum(prob * log_prob, dim=1, keepdim=True)
# dist = torch.distributions.Categorical(prob)
# if action is None:
# action = dist.sample()
# log_prob = dist.log_prob(action).unsqueeze(1)
# return action, log_prob, entropy_loss.mean(0), value
#
# def tensor(self, x):
# return self.network.tensor(x)
#
# def zero_grad(self):
# self.opt.zero_grad()
#
# def parameters(self):
# return self.network.parameters()
#
# def step(self):
# self.opt.step()
#
# def state_dict(self):
# return self.network.state_dict()
#
# def load_state_dict(self, state_dicts):
# self.network.load_state_dict(state_dicts)
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)