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b06c604a51
* dqn results ++ * add scale * hour * fix * small dqn table * update * steps * upd * apex * up * add apex results * tip
183 lines
14 KiB
ReStructuredText
183 lines
14 KiB
ReStructuredText
RLlib Algorithms
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================
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High-throughput architectures
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Distributed Prioritized Experience Replay (Ape-X)
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-------------------------------------------------
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`[paper] <https://arxiv.org/abs/1803.00933>`__
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`[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/dqn/apex.py>`__
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Ape-X variations of DQN and DDPG (`APEX_DQN <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/dqn/apex.py>`__, `APEX_DDPG <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/ddpg/apex.py>`__ in RLlib) use a single GPU learner and many CPU workers for experience collection. Experience collection can scale to hundreds of CPU workers due to the distributed prioritization of experience prior to storage in replay buffers.
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Tuned examples: `PongNoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-apex.yaml>`__, `Pendulum-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pendulum-apex-ddpg.yaml>`__, `MountainCarContinuous-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/mountaincarcontinuous-apex-ddpg.yaml>`__, `{BeamRider,Breakout,Qbert,SpaceInvaders}NoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-apex.yaml>`__.
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**Atari results @10M steps**: `more details <https://github.com/ray-project/rl-experiments>`__
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============= ================================ ========================================
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Atari env RLlib Ape-X 8-workers Mnih et al Async DQN 16-workers
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============= ================================ ========================================
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BeamRider 6134 ~6000
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Breakout 123 ~50
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Qbert 15302 ~1200
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SpaceInvaders 686 ~600
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============= ================================ ========================================
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**Scalability**:
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============= ================================ ========================================
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Atari env RLlib Ape-X 8-workers @1 hour Mnih et al Async DQN 16-workers @1 hour
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============= ================================ ========================================
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BeamRider 4873 ~1000
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Breakout 77 ~10
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Qbert 4083 ~500
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SpaceInvaders 646 ~300
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============= ================================ ========================================
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.. figure:: apex.png
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Ape-X using 32 workers in RLlib vs vanilla DQN (orange) and A3C (blue) on PongNoFrameskip-v4.
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Importance Weighted Actor-Learner Architecture (IMPALA)
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-------------------------------------------------------
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`[paper] <https://arxiv.org/abs/1802.01561>`__
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`[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/impala/impala.py>`__
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In IMPALA, a central learner runs SGD in a tight loop while asynchronously pulling sample batches from many actor processes. RLlib's IMPALA implementation uses DeepMind's reference `V-trace code <https://github.com/deepmind/scalable_agent/blob/master/vtrace.py>`__. Note that we do not provide a deep residual network out of the box, but one can be plugged in as a `custom model <rllib-models.html#custom-models>`__.
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Tuned examples: `PongNoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-impala.yaml>`__, `vectorized configuration <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-impala-vectorized.yaml>`__, `{BeamRider,Breakout,Qbert,SpaceInvaders}NoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-impala.yaml>`__
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**Atari results @10M steps**: `more details <https://github.com/ray-project/rl-experiments>`__
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============= ================================== ====================================
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Atari env RLlib IMPALA 32-workers Mnih et al A3C 16-workers
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============= ================================== ====================================
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BeamRider 2071 ~3000
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Breakout 385 ~150
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Qbert 4068 ~1000
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SpaceInvaders 719 ~600
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============= ================================== ====================================
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**Scalability:**
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============= =============================== =================================
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Atari env RLlib IMPALA 32-workers @1 hour Mnih et al A3C 16-workers @1 hour
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============= =============================== =================================
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BeamRider 3181 ~1000
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Breakout 538 ~10
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Qbert 10850 ~500
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SpaceInvaders 843 ~300
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============= =============================== =================================
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.. figure:: impala.png
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IMPALA solves Atari several times faster than A2C / A3C, with similar sample efficiency. Here IMPALA scales from 16 to 128 workers to solve PongNoFrameskip-v4 in ~8 minutes.
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Gradient-based
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~~~~~~~~~~~~~~
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Advantage Actor-Critic (A2C, A3C)
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---------------------------------
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`[paper] <https://arxiv.org/abs/1602.01783>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/a3c/a3c.py>`__
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RLlib implements A2C and A3C using SyncSamplesOptimizer and AsyncGradientsOptimizer respectively for policy optimization. These algorithms scale to up to 16-32 worker processes depending on the environment. Both a TensorFlow (LSTM), and PyTorch version are available.
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Tuned examples: `PongDeterministic-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-a3c.yaml>`__, `PyTorch version <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-a3c-pytorch.yaml>`__, `{BeamRider,Breakout,Qbert,SpaceInvaders}NoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-a2c.yaml>`__
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.. tip::
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Consider using `IMPALA <#importance-weighted-actor-learner-architecture-impala>`__ for faster training with similar timestep efficiency.
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**Atari results @10M steps**: `more details <https://github.com/ray-project/rl-experiments>`__
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============= ======================== ==============================
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Atari env RLlib A2C 5-workers Mnih et al A3C 16-workers
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============= ======================== ==============================
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BeamRider 1401 ~3000
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Breakout 374 ~150
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Qbert 3620 ~1000
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SpaceInvaders 692 ~600
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============= ======================== ==============================
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Deep Deterministic Policy Gradients (DDPG)
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------------------------------------------
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`[paper] <https://arxiv.org/abs/1509.02971>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/ddpg/ddpg.py>`__
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DDPG is implemented similarly to DQN (below). The algorithm can be scaled by increasing the number of workers, switching to AsyncGradientsOptimizer, or using Ape-X.
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Tuned examples: `Pendulum-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pendulum-ddpg.yaml>`__, `MountainCarContinuous-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/mountaincarcontinuous-ddpg.yaml>`__, `HalfCheetah-v2 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/halfcheetah-ddpg.yaml>`__
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Deep Q Networks (DQN, Rainbow)
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------------------------------
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`[paper] <https://arxiv.org/abs/1312.5602>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/dqn/dqn.py>`__
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RLlib DQN is implemented using the SyncReplayOptimizer. The algorithm can be scaled by increasing the number of workers, using the AsyncGradientsOptimizer for async DQN, or using Ape-X. Memory usage is reduced by compressing samples in the replay buffer with LZ4. All of the DQN improvements evaluated in `Rainbow <https://arxiv.org/abs/1710.02298>`__ are available, though not all are enabled by default.
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Tuned examples: `PongDeterministic-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-dqn.yaml>`__, `Rainbow configuration <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-rainbow.yaml>`__, `{BeamRider,Breakout,Qbert,SpaceInvaders}NoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-basic-dqn.yaml>`__, `with Dueling and Double-Q <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-duel-ddqn.yaml>`__, `with Distributional DQN <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-dist-dqn.yaml>`__.
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.. tip::
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Consider using `Ape-X <#distributed-prioritized-experience-replay-ape-x>`__ for faster training with similar timestep efficiency.
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**Atari results @10M steps**: `more details <https://github.com/ray-project/rl-experiments>`__
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============= ======================== ============================= ============================== ===============================
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Atari env RLlib DQN RLlib Dueling DDQN RLlib Dist. DQN Hessel et al. DQN
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============= ======================== ============================= ============================== ===============================
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BeamRider 2869 1910 4447 ~2000
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Breakout 287 312 410 ~150
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Qbert 3921 7968 15780 ~4000
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SpaceInvaders 650 1001 1025 ~500
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============= ======================== ============================= ============================== ===============================
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Policy Gradients
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----------------
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`[paper] <https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/pg/pg.py>`__ We include a vanilla policy gradients implementation as an example algorithm. This is usually outperformed by PPO.
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Tuned examples: `CartPole-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/regression_tests/cartpole-pg.yaml>`__
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Proximal Policy Optimization (PPO)
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----------------------------------
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`[paper] <https://arxiv.org/abs/1707.06347>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/ppo/ppo.py>`__
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PPO's clipped objective supports multiple SGD passes over the same batch of experiences. RLlib's multi-GPU optimizer pins that data in GPU memory to avoid unnecessary transfers from host memory, substantially improving performance over a naive implementation. RLlib's PPO scales out using multiple workers for experience collection, and also with multiple GPUs for SGD.
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Tuned examples: `Humanoid-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/humanoid-ppo-gae.yaml>`__, `Hopper-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/hopper-ppo.yaml>`__, `Pendulum-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pendulum-ppo.yaml>`__, `PongDeterministic-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-ppo.yaml>`__, `Walker2d-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/walker2d-ppo.yaml>`__, `{BeamRider,Breakout,Qbert,SpaceInvaders}NoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-ppo.yaml>`__
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**Atari results**: `more details <https://github.com/ray-project/rl-experiments>`__
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============= ============== ============== ==================
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Atari env RLlib PPO @10M RLlib PPO @25M Baselines PPO @10M
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============= ============== ============== ==================
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BeamRider 2807 4480 ~1800
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Breakout 104 201 ~250
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Qbert 11085 14247 ~14000
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SpaceInvaders 671 944 ~800
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============= ============== ============== ==================
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**Scalability:**
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.. figure:: ppo.png
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:width: 500px
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RLlib's multi-GPU PPO scales to multiple GPUs and hundreds of CPUs on solving the Humanoid-v1 task. Here we compare against a reference MPI-based implementation.
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Derivative-free
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~~~~~~~~~~~~~~~
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Augmented Random Search (ARS)
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-----------------------------
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`[paper] <https://arxiv.org/abs/1803.07055>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/ars/ars.py>`__
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ARS is a random search method for training linear policies for continuous control problems. Code here is adapted from https://github.com/modestyachts/ARS to integrate with RLlib APIs.
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Tuned examples: `CartPole-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/regression_tests/cartpole-ars.yaml>`__, `Swimmer-v2 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/swimmer-ars.yaml>`__
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Evolution Strategies
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--------------------
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`[paper] <https://arxiv.org/abs/1703.03864>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/es/es.py>`__
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Code here is adapted from https://github.com/openai/evolution-strategies-starter to execute in the distributed setting with Ray.
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Tuned examples: `Humanoid-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/humanoid-es.yaml>`__
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**Scalability:**
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.. figure:: es.png
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:width: 500px
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RLlib's ES implementation scales further and is faster than a reference Redis implementation on solving the Humanoid-v1 task.
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