From f6d0c4d2609d5efb55e220a51b99160aa3ab0874 Mon Sep 17 00:00:00 2001
From: Shangtong Zhang <zhangshangtong.cpp@icloud.com>
Date: Thu, 17 May 2018 23:37:50 -0600
Subject: [PATCH] Remove unused wrapper

---
 deep_rl/network/network_utils.py | 89 --------------------------------
 1 file changed, 89 deletions(-)

diff --git a/deep_rl/network/network_utils.py b/deep_rl/network/network_utils.py
index daadfe8..a0d303c 100644
--- a/deep_rl/network/network_utils.py
+++ b/deep_rl/network/network_utils.py
@@ -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)