Implement RandomPolicy

rl4co/models/nn/utils.py

@@ -1,5 +1,10 @@
+from typing import Union
+
 import torch
+import torch.nn as nn
+from tensordict import TensorDict
 
+from rl4co.envs import RL4COEnvBase, get_env
 from rl4co.utils import get_pylogger
 
 log = get_pylogger(__name__)
@@ -14,9 +19,7 @@ def get_log_likelihood(log_p, actions, mask, return_sum: bool = True):
     if mask is not None:
         log_p[~mask] = 0
 
-    assert (
-        log_p > -1000
-    ).data.all(), "Logprobs should not be -inf, check sampling procedure!"
+    assert (log_p > -1000).data.all(), "Logprobs should not be -inf, check sampling procedure!"
 
     # Calculate log_likelihood
     if return_sum:
@@ -69,3 +72,31 @@ def rollout(env, td, policy):
         td,
         torch.stack(actions, dim=1),
     )
+
+
+class RandomPolicy(nn.Module):
+
+    """
+    Random Policy Class that randomly select actions from the action space
+    This policy can be useful to check the sanity of the environment during development
+
+    We match the function signature of forward to the one of the AutoregressivePolicy class
+
+    """
+
+    def __init__(self, env_name=None):
+        super().__init__()
+        self.env_name = env_name
+
+    def forward(
+        self,
+        td: TensorDict,
+        env: Union[str, RL4COEnvBase] = None,
+    ):
+        # Instantiate environment if needed
+        if isinstance(env, str) or env is None:
+            env_name = self.env_name if env is None else env
+            log.info(f"Instantiated environment not provided; instantiating {env_name}")
+            env = get_env(env_name)
+
+        return rollout(env, td, random_policy)