ray-project--ray/doc/source/rllib/doc_code/replay_buffer_demo.py

# Demonstration of RLlib's ReplayBuffer workflow

from typing import Optional
import random
import numpy as np

from ray import tune
from ray.rllib.utils.replay_buffers import ReplayBuffer, StorageUnit
from ray.rllib.utils.annotations import override
from ray.rllib.utils.typing import SampleBatchType
from ray.rllib.utils.replay_buffers.utils import validate_buffer_config
from ray.rllib.examples.envs.classes.random_env import RandomEnv
from ray.rllib.policy.sample_batch import SampleBatch, concat_samples
from ray.rllib.algorithms.dqn.dqn import DQNConfig


# __sphinx_doc_replay_buffer_type_specification__begin__
config = (
    DQNConfig()
    .api_stack(
        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
    )
    .training(replay_buffer_config={"type": ReplayBuffer})
)

another_config = (
    DQNConfig()
    .api_stack(
        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
    )
    .training(replay_buffer_config={"type": "ReplayBuffer"})
)


yet_another_config = (
    DQNConfig()
    .api_stack(
        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
    )
    .training(
        replay_buffer_config={"type": "ray.rllib.utils.replay_buffers.ReplayBuffer"}
    )
)

validate_buffer_config(config)
validate_buffer_config(another_config)
validate_buffer_config(yet_another_config)

# After validation, all three configs yield the same effective config
assert (
    config.replay_buffer_config
    == another_config.replay_buffer_config
    == yet_another_config.replay_buffer_config
)

# __sphinx_doc_replay_buffer_type_specification__end__


# __sphinx_doc_replay_buffer_basic_interaction__begin__
# We choose fragments because it does not impose restrictions on our batch to be added
buffer = ReplayBuffer(capacity=2, storage_unit=StorageUnit.FRAGMENTS)
dummy_batch = SampleBatch({"a": [1], "b": [2]})
buffer.add(dummy_batch)
buffer.sample(2)
# Because elements can be sampled multiple times, we receive a concatenated version
# of dummy_batch `{a: [1, 1], b: [2, 2,]}`.
# __sphinx_doc_replay_buffer_basic_interaction__end__


# __sphinx_doc_replay_buffer_own_buffer__begin__
class LessSampledReplayBuffer(ReplayBuffer):
    @override(ReplayBuffer)
    def sample(
        self, num_items: int, evict_sampled_more_then: int = 30, **kwargs
    ) -> Optional[SampleBatchType]:
        """Evicts experiences that have been sampled > evict_sampled_more_then times."""
        idxes = [random.randint(0, len(self) - 1) for _ in range(num_items)]
        often_sampled_idxes = list(
            filter(lambda x: self._hit_count[x] >= evict_sampled_more_then, set(idxes))
        )

        sample = self._encode_sample(idxes)
        self._num_timesteps_sampled += sample.count

        for idx in often_sampled_idxes:
            del self._storage[idx]
            self._hit_count = np.append(
                self._hit_count[:idx], self._hit_count[idx + 1 :]
            )

        return sample


config = (
    DQNConfig()
    .api_stack(
        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
    )
    .environment(env="CartPole-v1")
    .training(replay_buffer_config={"type": LessSampledReplayBuffer})
)

tune.Tuner(
    "DQN",
    param_space=config,
    run_config=tune.RunConfig(
        stop={"training_iteration": 1},
    ),
).fit()
# __sphinx_doc_replay_buffer_own_buffer__end__

# __sphinx_doc_replay_buffer_advanced_usage_storage_unit__begin__
# This line will make our buffer store only complete episodes found in a batch
config.training(replay_buffer_config={"storage_unit": StorageUnit.EPISODES})

less_sampled_buffer = LessSampledReplayBuffer(**config.replay_buffer_config)

# Gather some random experiences
env = RandomEnv()
terminated = truncated = False
batch = SampleBatch({})
t = 0
while not terminated and not truncated:
    obs, reward, terminated, truncated, info = env.step([0, 0])
    # Note that in order for RLlib to find out about start and end of an episode,
    # "t" and "terminateds" have to properly mark an episode's trajectory
    one_step_batch = SampleBatch(
        {
            "obs": [obs],
            "t": [t],
            "reward": [reward],
            "terminateds": [terminated],
            "truncateds": [truncated],
        }
    )
    batch = concat_samples([batch, one_step_batch])
    t += 1

less_sampled_buffer.add(batch)
for i in range(10):
    assert len(less_sampled_buffer._storage) == 1
    less_sampled_buffer.sample(num_items=1, evict_sampled_more_then=9)

assert len(less_sampled_buffer._storage) == 0
# __sphinx_doc_replay_buffer_advanced_usage_storage_unit__end__


# __sphinx_doc_replay_buffer_advanced_usage_underlying_buffers__begin__
config = (
    DQNConfig()
    .api_stack(
        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
    )
    .training(
        replay_buffer_config={
            "type": "MultiAgentReplayBuffer",
            "underlying_replay_buffer_config": {
                "type": LessSampledReplayBuffer,
                # We can specify the default call argument
                # for the sample method of the underlying buffer method here.
                "evict_sampled_more_then": 20,
            },
        }
    )
    .environment(env="CartPole-v1")
)

tune.Tuner(
    "DQN",
    param_space=config.to_dict(),
    run_config=tune.RunConfig(
        stop={"env_runners/episode_return_mean": 40, "training_iteration": 7},
    ),
).fit()
# __sphinx_doc_replay_buffer_advanced_usage_underlying_buffers__end__