deleted leftover file

ml/model.py

@@ -1,49 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.distributions import Normal
-
-
-class ActorCritic(nn.Module):
-    def __init__(self, obs_dim: int, action_dim: int, hidden_sizes=(256, 256)):
-        super().__init__()
-        self.backbone = nn.Sequential(
-            nn.Linear(obs_dim, hidden_sizes[0]),
-            nn.ReLU(),
-            nn.Linear(hidden_sizes[0], hidden_sizes[1]),
-            nn.ReLU(),
-        )
-
-        self.mean_head = nn.Linear(hidden_sizes[1], action_dim)
-        self.value_head = nn.Linear(hidden_sizes[1], 1)
-        self.log_std = nn.Parameter(torch.zeros(action_dim, dtype=torch.float32))
-
-    def forward(self, obs: torch.Tensor):
-        x = self.backbone(obs)
-        mean = self.mean_head(x)
-        std = self.log_std.exp()
-        value = self.value_head(x).squeeze(-1)
-        return mean, std, value
-
-    def get_action(self, obs: torch.Tensor):
-        mean, std, value = self.forward(obs)
-        dist = Normal(mean, std)
-        action = dist.sample()
-        log_prob = dist.log_prob(action).sum(-1)
-        return action, log_prob, value
-
-    def evaluate_actions(self, obs: torch.Tensor, actions: torch.Tensor):
-        mean, std, value = self.forward(obs)
-        dist = Normal(mean, std)
-        log_prob = dist.log_prob(actions).sum(-1)
-        entropy = dist.entropy().sum(-1)
-        return value, log_prob, entropy
-
-    def save(self, path: str):
-        torch.save(self.state_dict(), path)
-
-    @classmethod
-    def load(cls, path: str, obs_dim: int, action_dim: int, hidden_sizes=(256, 256)):
-        model = cls(obs_dim, action_dim, hidden_sizes)
-        model.load_state_dict(torch.load(path, map_location=torch.device("cpu")))
-        model.eval()
-        return model