[RLlib Contrib] APEX DQN

.buildkite/pipeline.ml.yml

@@ -463,23 +463,28 @@
       doc/...
 
 
-- label: ":exploding_death_star: RLlib Contrib: A3C Tests"
+- label: ":exploding_death_star: RLlib Contrib: A2C Tests"
   conditions: ["NO_WHEELS_REQUIRED", "RAY_CI_RLLIB_CONTRIB_AFFECTED"]
   commands:
     - cleanup() { if [ "${BUILDKITE_PULL_REQUEST}" = "false" ]; then ./ci/build/upload_build_info.sh; fi }; trap cleanup EXIT
-    - (cd rllib_contrib/a3c && pip install -r requirements.txt && pip install -e .)
+    - conda deactivate
+    - conda create -n rllib_contrib python=3.8 -y
+    - conda activate rllib_contrib
+    - (cd rllib_contrib/a2c && pip install -r requirements.txt && pip install -e ".[development"])
     - ./ci/env/env_info.sh
-    - pytest rllib_contrib/a3c/tests/test_a3c.py
+    - pytest rllib_contrib/a2c/tests/
+    - python rllib_contrib/a2c/examples/a2c_cartpole_v1.py --run-as-test
 
-- label: ":exploding_death_star: RLlib Contrib: MAML Tests"
+- label: ":exploding_death_star: RLlib Contrib: A3C Tests"
   conditions: ["NO_WHEELS_REQUIRED", "RAY_CI_RLLIB_CONTRIB_AFFECTED"]
   commands:
     - cleanup() { if [ "${BUILDKITE_PULL_REQUEST}" = "false" ]; then ./ci/build/upload_build_info.sh; fi }; trap cleanup EXIT
-
-    - source /root/.bashrc
-    - (cd rllib_contrib/maml && pip install -r requirements.txt && pip install -e .)
+    - conda deactivate
+    - conda create -n rllib_contrib python=3.8 -y
+    - conda activate rllib_contrib
+    - (cd rllib_contrib/a3c && pip install -r requirements.txt && pip install -e ".[development"])
     - ./ci/env/env_info.sh
-    - pytest rllib_contrib/maml/tests/test_maml.py
+    - pytest rllib_contrib/a3c/tests/test_a3c.py
 
 - label: ":exploding_death_star: RLlib Contrib: Alpha Star Tests"
   conditions: ["NO_WHEELS_REQUIRED", "RAY_CI_RLLIB_CONTRIB_AFFECTED"]
@@ -490,29 +495,56 @@
     - pytest rllib_contrib/alpha_star/tests/
     - python rllib_contrib/alpha_star/examples/multi-agent-cartpole-alpha-star.py --run-as-test
 
-- label: ":exploding_death_star: RLlib Contrib: A2C Tests"
+- label: ":exploding_death_star: RLlib Contrib: APEX DQN Tests"
   conditions: ["NO_WHEELS_REQUIRED", "RAY_CI_RLLIB_CONTRIB_AFFECTED"]
   commands:
     - cleanup() { if [ "${BUILDKITE_PULL_REQUEST}" = "false" ]; then ./ci/build/upload_build_info.sh; fi }; trap cleanup EXIT
-    - (cd rllib_contrib/a2c && pip install -r requirements.txt && pip install -e .)
+    - conda deactivate
+    - conda create -n rllib_contrib python=3.8 -y
+    - conda activate rllib_contrib
+    - (cd rllib_contrib/apex_dqn && pip install -r requirements.txt && pip install -e ".[development"])
     - ./ci/env/env_info.sh
-    - pytest rllib_contrib/a2c/tests/
-    - python rllib_contrib/a2c/examples/a2c_cartpole_v1.py --run-as-test
+    - pytest rllib_contrib/apex_dqn/tests/
+    - python rllib_contrib/apex_dqn/examples/apex_dqn_cartpole_v1.py --run-as-test
+
+- label: ":exploding_death_star: RLlib Contrib: DDPG Tests"
+  conditions: ["NO_WHEELS_REQUIRED", "RAY_CI_RLLIB_CONTRIB_AFFECTED"]
+  commands:
+    - cleanup() { if [ "${BUILDKITE_PULL_REQUEST}" = "false" ]; then ./ci/build/upload_build_info.sh; fi }; trap cleanup EXIT
+    - conda deactivate
+    - conda create -n rllib_contrib python=3.8 -y
+    - conda activate rllib_contrib
+    - (cd rllib_contrib/ddpg && pip install -r requirements.txt && pip install -e ".[development"])
+    - ./ci/env/env_info.sh
+    - pytest rllib_contrib/ddpg/tests/
+    - python rllib_contrib/ddpg/examples/ddpg_pendulum_v1.py --run-as-test
 
-- label: ":exploding_death_star: RLlib Contrib: R2D2 Tests"
+- label: ":exploding_death_star: RLlib Contrib: MAML Tests"
   conditions: ["NO_WHEELS_REQUIRED", "RAY_CI_RLLIB_CONTRIB_AFFECTED"]
   commands:
     - cleanup() { if [ "${BUILDKITE_PULL_REQUEST}" = "false" ]; then ./ci/build/upload_build_info.sh; fi }; trap cleanup EXIT
-    - (cd rllib_contrib/r2d2 && pip install -r requirements.txt && pip install -e .)
+
+    # Install mujoco necessary for the testing environments
+    - conda deactivate
+    - conda create -n rllib_contrib python=3.8 -y
+    - conda activate rllib_contrib
+    - sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3 patchelf -y
+    - [ ! -d "/root/.mujoco" ] && mkdir -p /root/.mujoco && wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz \
+      && mv mujoco210-linux-x86_64.tar.gz /root/.mujoco/. && \
+      (cd /root/.mujoco && tar -xf /root/.mujoco/mujoco210-linux-x86_64.tar.gz)
+    - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/.mujoco/mujoco210/bin
+    - (cd rllib_contrib/maml && pip install -r requirements.txt && pip install -e ".[development"])
     - ./ci/env/env_info.sh
-    - pytest rllib_contrib/r2d2/tests/
-    - python rllib_contrib/r2d2/examples/r2d2_stateless_cartpole.py --run-as-test
+    - pytest rllib_contrib/maml/tests/test_maml.py
 
-- label: ":exploding_death_star: RLlib Contrib: DDPG Tests"
+- label: ":exploding_death_star: RLlib Contrib: R2D2 Tests"
   conditions: ["NO_WHEELS_REQUIRED", "RAY_CI_RLLIB_CONTRIB_AFFECTED"]
   commands:
     - cleanup() { if [ "${BUILDKITE_PULL_REQUEST}" = "false" ]; then ./ci/build/upload_build_info.sh; fi }; trap cleanup EXIT
-    - (cd rllib_contrib/ddpg && pip install -r requirements.txt && pip install -e .)
+    - conda deactivate
+    - conda create -n rllib_contrib python=3.8 -y
+    - conda activate rllib_contrib
+    - (cd rllib_contrib/r2d2 && pip install -r requirements.txt && pip install -e ".[development"])
     - ./ci/env/env_info.sh
-    - pytest rllib_contrib/ddpg/tests/
-    - python rllib_contrib/ddpg/examples/ddpg_pendulum_v1.py --run-as-test
+    - pytest rllib_contrib/r2d2/tests/
+    - python rllib_contrib/r2d2/examples/r2d2_stateless_cartpole.py --run-as-test

rllib_contrib/TOC.md

@@ -2,15 +2,15 @@
 
 
 * [A3C](./a3c)
-* [MAML](./maml)
-* [Alpha Star](./alpha_star)
 * [A2C](./a2c)
+* [Alpha Star](./alpha_star)
+* [APEX DQN](./apex_dqn/)
 * [DDPG](./ddpg)
+* [MAML](./maml)
 * [R2D2](./r2d2)
 
 
 
-
 # Example Use-cases
 
 * [Using TF-GNN for encoding graph spaces in RLlib using Tensorflow](https://github.com/kk-55/tf-gnn-example-for-rllib)

rllib_contrib/a3c/pyproject.toml

@@ -12,7 +12,7 @@ version = "0.1.0"
 description = ""
 readme = "README.md"
 requires-python = ">=3.7, <3.11"
-dependencies = ["gym[accept-rom-license]", "gymnasium[mujoco]==0.26.3", "higher", "ray[rllib]==2.3.1"]
+dependencies = ["gym[accept-rom-license]", "gymnasium[mujoco]==0.26.3", "ray[rllib]==2.3.1"]
 
 [project.optional-dependencies]
 development = ["pytest>=7.2.2", "pre-commit==2.21.0", "tensorflow==2.11.0", "torch==1.12.0"]

rllib_contrib/apex_dqn/README.md

@@ -0,0 +1,19 @@
+# APEX DQN (Distributed Prioritized Experience Replay)
+
+[APEX DQN](https://arxiv.org/pdf/1803.00933.pdf) Distributed Prioritized Experience Replay is an algorithm that decouples
+active learning from sampling. Actors interact with their own instances of the environment by selecting actions according
+to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to
+focus only on the most significant data generated by the actors.
+
+## Installation
+
+```
+conda create -n rllib-apex-dqn python=3.10
+conda activate rllib-apex-dqn
+pip install -r requirements.txt
+pip install -e '.[development]'
+```
+
+## Usage
+
+[APEX-DQN Example]()

rllib_contrib/apex_dqn/examples/apex_dqn_cartpole_v1.py

@@ -0,0 +1,58 @@
+import argparse
+
+from rllib_apex_dqn.apex_dqn import ApexDQN, ApexDQNConfig
+
+import ray
+from ray import air, tune
+from ray.rllib.utils.test_utils import check_learning_achieved
+
+
+def get_cli_args():
+    """Create CLI parser and return parsed arguments"""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--run-as-test", action="store_true", default=False)
+    args = parser.parse_args()
+    print(f"Running with following CLI args: {args}")
+    return args
+
+
+if __name__ == "__main__":
+    args = get_cli_args()
+
+    ray.init()
+
+    config = (
+        ApexDQNConfig()
+        .rollouts(num_rollout_workers=3)
+        .environment("CartPole-v1")
+        .training(
+            replay_buffer_config={
+                "type": "MultiAgentPrioritizedReplayBuffer",
+                "capacity": 20000,
+            },
+            num_steps_sampled_before_learning_starts=1000,
+            optimizer={"num_replay_buffer_shards": 2},
+            target_network_update_freq=500,
+            training_intensity=4,
+        )
+        .resources(num_gpus=0)
+        .reporting(min_sample_timesteps_per_iteration=1000, min_time_s_per_iteration=5)
+    )
+
+    stop_reward = 150.0
+
+    tuner = tune.Tuner(
+        ApexDQN,
+        param_space=config.to_dict(),
+        run_config=air.RunConfig(
+            stop={
+                "sampler_results/episode_reward_mean": stop_reward,
+                "timesteps_total": 250000,
+            },
+            failure_config=air.FailureConfig(fail_fast="raise"),
+        ),
+    )
+    results = tuner.fit()
+
+    if args.run_as_test:
+        check_learning_achieved(results, stop_reward)

rllib_contrib/apex_dqn/pyproject.toml

@@ -0,0 +1,18 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[project]
+name = "rllib-apex-dqn"
+authors = [{name = "Anyscale Inc."}]
+version = "0.1.0"
+description = ""
+readme = "README.md"
+requires-python = ">=3.7, <3.11"
+dependencies = ["gymnasium[atari]", "ray[rllib]==2.5.0"]
+
+[project.optional-dependencies]
+development = ["pytest>=7.2.2", "pre-commit==2.21.0", "tensorflow==2.11.0", "torch==1.12.0"]

rllib_contrib/apex_dqn/requirements.txt

@@ -0,0 +1,2 @@
+tensorflow==2.11.0
+torch==1.12.0

rllib_contrib/apex_dqn/src/rllib_apex_dqn/apex_dqn/__init__.py

@@ -0,0 +1,7 @@
+from rllib_apex_dqn.apex_dqn.apex_dqn import ApexDQN, ApexDQNConfig
+
+from ray.tune.registry import register_trainable
+
+__all__ = ["ApexDQNConfig", "ApexDQN"]
+
+register_trainable("rllib-contrib-apex-dqn", ApexDQN)