#641 code review: refactor PebbleStateEntropyReward so that inner Rew…

…ardNets are initialized in constructor

src/imitation/algorithms/pebble/entropy_reward.py

@@ -18,42 +18,42 @@
 from imitation.util.networks import RunningNorm
 
 
-class PebbleRewardPhase(enum.Enum):
-    """States representing different behaviors for PebbleStateEntropyReward."""
-
-    UNSUPERVISED_EXPLORATION = enum.auto()  # Entropy based reward
-    POLICY_AND_REWARD_LEARNING = enum.auto()  # Learned reward
-
-
 class InsufficientObservations(RuntimeError):
     pass
 
 
-class EntropyRewardNet(RewardNet):
+class EntropyRewardNet(RewardNet, ReplayBufferAwareRewardFn):
     def __init__(
         self,
         nearest_neighbor_k: int,
-        replay_buffer_view: ReplayBufferView,
         observation_space: gym.Space,
         action_space: gym.Space,
         normalize_images: bool = True,
+        replay_buffer_view: Optional[ReplayBufferView] = None,
     ):
         """Initialize the RewardNet.
 
         Args:
+            nearest_neighbor_k: Parameter for entropy computation (see
+                compute_state_entropy())
             observation_space: the observation space of the environment
             action_space: the action space of the environment
             normalize_images: whether to automatically normalize
                 image observations to [0, 1] (from 0 to 255). Defaults to True.
+            replay_buffer_view: Replay buffer view with observations to compare
+                against when computing entropy. If None is given, the buffer needs to
+                be set with on_replay_buffer_initialized() before EntropyRewardNet can
+                be used
         """
         super().__init__(observation_space, action_space, normalize_images)
         self.nearest_neighbor_k = nearest_neighbor_k
         self._replay_buffer_view = replay_buffer_view
 
-    def set_replay_buffer(self, replay_buffer: ReplayBufferRewardWrapper):
-        """This method needs to be called after unpickling.
+    def on_replay_buffer_initialized(self, replay_buffer: ReplayBufferRewardWrapper):
+        """Sets replay buffer.
 
-        See also __getstate__() / __setstate__()
+        This method needs to be called, e.g., after unpickling.
+        See also __getstate__() / __setstate__().
         """
         assert self.observation_space == replay_buffer.observation_space
         assert self.action_space == replay_buffer.action_space
@@ -111,6 +111,13 @@ def __setstate__(self, state):
         self._replay_buffer_view = None
 
 
+class PebbleRewardPhase(enum.Enum):
+    """States representing different behaviors for PebbleStateEntropyReward."""
+
+    UNSUPERVISED_EXPLORATION = enum.auto()  # Entropy based reward
+    POLICY_AND_REWARD_LEARNING = enum.auto()  # Learned reward
+
+
 class PebbleStateEntropyReward(ReplayBufferAwareRewardFn):
     """Reward function for implementation of the PEBBLE learning algorithm.
 
@@ -126,14 +133,15 @@ class PebbleStateEntropyReward(ReplayBufferAwareRewardFn):
     reward is returned.
 
     The second phase requires that a buffer with observations to compare against is
-    supplied with set_replay_buffer() or on_replay_buffer_initialized().
-    To transition to the last phase, unsupervised_exploration_finish() needs
-    to be called.
+    supplied with on_replay_buffer_initialized(). To transition to the last phase,
+    unsupervised_exploration_finish() needs to be called.
     """
 
     def __init__(
         self,
         learned_reward_fn: RewardFn,
+        observation_space: gym.Space,
+        action_space: gym.Space,
         nearest_neighbor_k: int = 5,
     ):
         """Builds this class.
@@ -146,28 +154,20 @@ def __init__(
         """
         self.learned_reward_fn = learned_reward_fn
         self.nearest_neighbor_k = nearest_neighbor_k
-
         self.state = PebbleRewardPhase.UNSUPERVISED_EXPLORATION
 
-        # These two need to be set with set_replay_buffer():
-        self._entropy_reward_net: Optional[EntropyRewardNet] = None
-        self._normalized_entropy_reward_net: Optional[RewardNet] = None
+        self._entropy_reward_net = EntropyRewardNet(
+            nearest_neighbor_k=self.nearest_neighbor_k,
+            observation_space=observation_space,
+            action_space=action_space,
+            normalize_images=False,
+        )
+        self._normalized_entropy_reward_net = NormalizedRewardNet(
+            self._entropy_reward_net, RunningNorm
+        )
 
     def on_replay_buffer_initialized(self, replay_buffer: ReplayBufferRewardWrapper):
-        if self._normalized_entropy_reward_net is None:
-            self._entropy_reward_net = EntropyRewardNet(
-                nearest_neighbor_k=self.nearest_neighbor_k,
-                replay_buffer_view=replay_buffer.buffer_view,
-                observation_space=replay_buffer.observation_space,
-                action_space=replay_buffer.action_space,
-                normalize_images=False,
-            )
-            self._normalized_entropy_reward_net = NormalizedRewardNet(
-                self._entropy_reward_net, RunningNorm
-            )
-        else:
-            assert self._entropy_reward_net is not None
-            self._entropy_reward_net.set_replay_buffer(replay_buffer)
+        self._entropy_reward_net.on_replay_buffer_initialized(replay_buffer)
 
     def unsupervised_exploration_finish(self):
         assert self.state == PebbleRewardPhase.UNSUPERVISED_EXPLORATION

src/imitation/scripts/train_preference_comparisons.py

@@ -74,7 +74,9 @@ def make_reward_function(
     if pebble_enabled:
         relabel_reward_fn = PebbleStateEntropyReward(
             relabel_reward_fn,  # type: ignore[assignment]
-            pebble_nearest_neighbor_k,
+            observation_space=reward_net.observation_space,
+            action_space=reward_net.action_space,
+            nearest_neighbor_k=pebble_nearest_neighbor_k,
         )
     return relabel_reward_fn
 

tests/algorithms/pebble/test_entropy_reward.py

@@ -5,8 +5,9 @@
 
 import numpy as np
 import torch as th
-from gym.spaces import Discrete,  Box
+from gym.spaces import Box
 from gym.spaces.space import Space
+
 from imitation.algorithms.pebble.entropy_reward import PebbleStateEntropyReward
 from imitation.policies.replay_buffer_wrapper import ReplayBufferView
 from imitation.util import util
@@ -23,7 +24,7 @@
 def test_pebble_entropy_reward_returns_entropy_for_pretraining(rng):
     all_observations = rng.random((BUFFER_SIZE, VENVS) + SPACE.shape)
 
-    reward_fn = PebbleStateEntropyReward(Mock(), K)
+    reward_fn = PebbleStateEntropyReward(Mock(), SPACE, SPACE, K)
     reward_fn.on_replay_buffer_initialized(
         replay_buffer_mock(
             ReplayBufferView(all_observations, lambda: slice(None)),
@@ -50,7 +51,7 @@ def test_pebble_entropy_reward_returns_normalized_values_for_pretraining():
         # only stats collection in this test
         m.side_effect = lambda obs, all_obs, k: obs
 
-        reward_fn = PebbleStateEntropyReward(Mock(), K)
+        reward_fn = PebbleStateEntropyReward(Mock(), SPACE, SPACE, K)
         all_observations = np.empty((BUFFER_SIZE, VENVS, *SPACE.shape))
         reward_fn.on_replay_buffer_initialized(
             replay_buffer_mock(
@@ -88,7 +89,7 @@ def test_pebble_entropy_reward_function_returns_learned_reward_after_pre_trainin
     expected_reward = np.ones(1)
     learned_reward_mock = Mock()
     learned_reward_mock.return_value = expected_reward
-    reward_fn = PebbleStateEntropyReward(learned_reward_mock)
+    reward_fn = PebbleStateEntropyReward(learned_reward_mock, SPACE, SPACE)
     # move all the way to the last state
     reward_fn.unsupervised_exploration_finish()
 
@@ -111,7 +112,7 @@ def test_pebble_entropy_reward_can_pickle():
     replay_buffer = ReplayBufferView(all_observations, lambda: slice(None))
 
     obs1 = np.random.rand(VENVS, *SPACE.shape)
-    reward_fn = PebbleStateEntropyReward(reward_fn_stub, K)
+    reward_fn = PebbleStateEntropyReward(reward_fn_stub, SPACE, SPACE, K)
     reward_fn.on_replay_buffer_initialized(replay_buffer_mock(replay_buffer, SPACE))
     reward_fn(obs1, PLACEHOLDER, PLACEHOLDER, PLACEHOLDER)
 

tests/algorithms/test_preference_comparisons.py

@@ -85,7 +85,7 @@ def pebble_agent_trainer(agent, reward_net, venv, rng, replay_buffer):
     replay_buffer_mock = Mock()
     replay_buffer_mock.buffer_view = replay_buffer
     replay_buffer_mock.obs_shape = (4,)
-    reward_fn = PebbleStateEntropyReward(reward_net.predict_processed)
+    reward_fn = PebbleStateEntropyReward(reward_net.predict_processed, venv.observation_space, venv.action_space)
     reward_fn.on_replay_buffer_initialized(replay_buffer_mock)
     return preference_comparisons.PebbleAgentTrainer(
         algorithm=agent,