Fix the replay buffer overflow issue (#75)

overload issue in replay buffer, add buffer test + move baselines test to separate folder

qdax/core/neuroevolution/buffers/buffer.py

@@ -343,20 +343,42 @@ def insert(self, transitions: Transition) -> ReplayBuffer:
         num_transitions = flattened_transitions.shape[0]
         max_replay_size = self.buffer_size
 
-        new_current_position = self.current_position + num_transitions
-        new_current_size = jnp.minimum(
-            self.current_size + num_transitions, max_replay_size
-        )
+        # Make sure update is not larger than the maximum replay size.
+        if num_transitions > max_replay_size:
+            raise ValueError(
+                "Trying to insert a batch of samples larger than the maximum replay "
+                f"size. num_samples: {num_transitions}, "
+                f"max replay size {max_replay_size}"
+            )
+
+        # get current position
+        position = self.current_position
+
+        # check if there is an overlap
+        roll = jnp.minimum(0, max_replay_size - position - num_transitions)
+
+        # roll the data to avoid overlap
+        data = jnp.roll(self.data, roll, axis=0)
+
+        # update the position accordingly
+        new_position = position + roll
+
+        # replace old data by the new one
         new_data = jax.lax.dynamic_update_slice_in_dim(
-            self.data,
+            data,
             flattened_transitions,
-            start_index=self.current_position % max_replay_size,
+            start_index=new_position,
             axis=0,
         )
 
+        # update the position and the size
+        new_position = (new_position + num_transitions) % max_replay_size
+        new_size = jnp.minimum(self.current_size + num_transitions, max_replay_size)
+
+        # update the replay buffer
         replay_buffer = self.replace(
-            current_position=new_current_position,
-            current_size=new_current_size,
+            current_position=new_position,
+            current_size=new_size,
             data=new_data,
         )
 

tests/core_test/neuroevolution_test/buffers_test/buffer_test.py

@@ -0,0 +1,96 @@
+import jax
+import jax.numpy as jnp
+import pytest
+
+from qdax.core.neuroevolution.buffers.buffer import QDTransition, ReplayBuffer
+
+
+def test_insert() -> None:
+    """Tests if an insert of a dummy transition results in a buffer of size 1"""
+    observation_size = 2
+    action_size = 8
+    buffer_size = 10
+
+    # Initialize buffer
+    dummy_transition = QDTransition.init_dummy(
+        observation_dim=observation_size,
+        action_dim=action_size,
+        descriptor_dim=0,
+    )
+    replay_buffer = ReplayBuffer.init(
+        buffer_size=buffer_size, transition=dummy_transition
+    )
+
+    replay_buffer = replay_buffer.insert(dummy_transition)
+    pytest.assume(replay_buffer.current_size == 1)
+
+
+def test_insert_batch() -> None:
+    """Tests if inserting transitions such that we exceed the max size
+    of the buffer leads to the desired behavior."""
+    observation_size = 2
+    action_size = 8
+    buffer_size = 5
+
+    # Initialize buffer
+    dummy_transition = QDTransition.init_dummy(
+        observation_dim=observation_size,
+        action_dim=action_size,
+        descriptor_dim=0,
+    )
+    replay_buffer = ReplayBuffer.init(
+        buffer_size=buffer_size, transition=dummy_transition
+    )
+
+    simple_transition = jax.tree_map(lambda x: x.repeat(3, axis=0), dummy_transition)
+    simple_transition = simple_transition.replace(rewards=jnp.arange(3))
+    data = QDTransition.from_flatten(replay_buffer.data, dummy_transition)
+    pytest.assume(
+        jnp.array_equal(data.rewards, jnp.array([jnp.nan] * 5), equal_nan=True).all()
+    )
+
+    replay_buffer = replay_buffer.insert(simple_transition)
+    data = QDTransition.from_flatten(replay_buffer.data, dummy_transition)
+    pytest.assume(
+        jnp.array_equal(
+            data.rewards, jnp.array([0, 1, 2, jnp.nan, jnp.nan]), equal_nan=True
+        ).all()
+    )
+
+    simple_transition_2 = simple_transition.replace(rewards=jnp.arange(3, 6))
+    replay_buffer = replay_buffer.insert(simple_transition_2)
+    data = QDTransition.from_flatten(replay_buffer.data, dummy_transition)
+    pytest.assume(
+        jnp.array_equal(data.rewards, jnp.array([1, 2, 3, 4, 5]), equal_nan=True).all()
+    )
+
+
+def test_sample() -> None:
+    """
+    Tests if sampled transitions have valid shape.
+    """
+    observation_size = 2
+    action_size = 8
+    buffer_size = 5
+
+    # Initialize buffer
+    dummy_transition = QDTransition.init_dummy(
+        observation_dim=observation_size,
+        action_dim=action_size,
+        descriptor_dim=0,
+    )
+    replay_buffer = ReplayBuffer.init(
+        buffer_size=buffer_size, transition=dummy_transition
+    )
+
+    simple_transition = jax.tree_map(lambda x: x.repeat(3, axis=0), dummy_transition)
+    simple_transition = simple_transition.replace(rewards=jnp.arange(3))
+
+    replay_buffer = replay_buffer.insert(simple_transition)
+    random_key = jax.random.PRNGKey(0)
+
+    samples, random_key = replay_buffer.sample(random_key, 3)
+
+    samples_shapes = jax.tree_map(lambda x: x.shape, samples)
+    transition_shapes = jax.tree_map(lambda x: x.shape, simple_transition)
+    pytest.assume((samples_shapes == transition_shapes))