work on policies

hopes/policy/action_probs.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from sab_ope.policy.policies import Policy
+from hopes.policy.policies import Policy
 
 
 def compute_action_probs_from_policy(policy: Policy, obs: np.ndarray) -> np.ndarray:

hopes/policy/policies.py

@@ -1,8 +1,26 @@
 from abc import ABC, abstractmethod
 
+import numpy as np
+
 
 class Policy(ABC):
 
     @abstractmethod
-    def log_likelihoods(self, obs):
+    def log_likelihoods(self, obs: np.ndarray) -> np.ndarray:
         raise NotImplementedError
+
+
+class RandomPolicy(Policy):
+
+    def __init__(self, num_actions: int):
+        assert num_actions > 0, "Number of actions must be positive."
+        self.num_actions = num_actions
+
+    def log_likelihoods(self, obs: np.ndarray) -> np.ndarray:
+        assert obs.ndim == 2, "Observations must have shape (batch_size, obs_dim)."
+        assert obs.shape[1] > 0, "Observations must have shape (batch_size, obs_dim)."
+
+        action_probs = np.random.rand(obs.shape[0], self.num_actions)
+        action_probs /= action_probs.sum(axis=1, keepdims=True)
+        return np.log(action_probs)
+

tests/test_policies.py

@@ -0,0 +1,33 @@
+import unittest
+
+import numpy as np
+
+from hopes.policy.action_probs import compute_action_probs_from_policy
+from hopes.policy.policies import RandomPolicy
+
+
+class TestPolicies(unittest.TestCase):
+
+    def test_rnd_policy(self):
+        rnd_policy = RandomPolicy(num_actions=3)
+
+        log_probs = rnd_policy.log_likelihoods(obs=np.random.rand(10, 5))
+        self.assertIsInstance(log_probs, np.ndarray)
+        self.assertEqual(log_probs.shape, (10, 3))
+        self.assertTrue(np.all(log_probs <= 0.0))
+        self.assertTrue(np.all(log_probs >= -np.inf))
+
+        act_probs = np.exp(log_probs)
+        self.assertTrue(np.all(act_probs >= 0.0))
+        self.assertTrue(np.all(act_probs <= 1.0))
+        self.assertTrue(np.allclose(act_probs.sum(axis=1), 1.0))
+
+    def test_compute_action_probs(self):
+        rnd_policy = RandomPolicy(num_actions=3)
+        act_probs = compute_action_probs_from_policy(rnd_policy, obs=np.random.rand(10, 5))
+
+        self.assertIsInstance(act_probs, np.ndarray)
+        self.assertEqual(act_probs.shape, (10, 3))
+        self.assertTrue(np.all(act_probs >= 0.0))
+        self.assertTrue(np.all(act_probs <= 1.0))
+        self.assertTrue(np.allclose(act_probs.sum(axis=1), 1.0))