"Make It Work Make It Right Make It Fast"

― KentBeck

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⚠️ This package is moved into ReinforcementLearning.jl (2021-05-06)

This project aims to provide some implementations of the most typical reinforcement learning algorithms.

Algorithms Implemented

• VPG (Vanilla Policy Gradient, with a baseline)
• DQN
• Prioritized DQN
• Rainbow
• IQN
• A2C/A2C with GAE/MAC
• PPO
• DDPG
• TD3
• SAC
• CFR/OS-MCCFR/ES-MCCFR/DeepCFR
• Minimax
• Behavior Cloning

If you are looking for tabular reinforcement learning algorithms, you may refer ReinforcementLearningAnIntroduction.jl.

Built-in Experiments

Some built-in experiments are exported to help new users to easily run benchmarks with one line. For experienced users, you are suggested to check the source code of those experiments and make changes as needed.

List of built-in experiments

• E`JuliaRL_BasicDQN_CartPole`
• E`JuliaRL_DQN_CartPole`
• E`JuliaRL_PrioritizedDQN_CartPole`
• E`JuliaRL_Rainbow_CartPole`
• E`JuliaRL_IQN_CartPole`
• E`JuliaRL_A2C_CartPole`
• E`JuliaRL_A2CGAE_CartPole` (Thanks to @sriram13m)
• E`JuliaRL_MAC_CartPole` (Thanks to @RajGhugare19)
• E`JuliaRL_PPO_CartPole`
• E`JuliaRL_VPG_CartPole` (Thanks to @norci)
• E`JuliaRL_VPG_Pendulum` (continuous action space)
• E`JuliaRL_VPG_PendulumD` (discrete action space)
• E`JuliaRL_DDPG_Pendulum`
• E`JuliaRL_TD3_Pendulum` (Thanks to @rbange)
• E`JuliaRL_SAC_Pendulum` (Thanks to @rbange)
• E`JuliaRL_PPO_Pendulum`
• E`JuliaRL_BasicDQN_MountainCar` (Thanks to @felixchalumeau)
• E`JuliaRL_DQN_MountainCar` (Thanks to @felixchalumeau)
• E`JuliaRL_Minimax_OpenSpiel(tic_tac_toe)`
• E`JuliaRL_TabularCFR_OpenSpiel(kuhn_poker)`
• E`JuliaRL_DeepCFR_OpenSpiel(leduc_poker)`
• E`JuliaRL_DQN_SnakeGame`
• E`JuliaRL_BC_CartPole`
• E`JuliaRL_BasicDQN_EmptyRoom`
• E`Dopamine_DQN_Atari(pong)`
• E`Dopamine_Rainbow_Atari(pong)`
• E`Dopamine_IQN_Atari(pong)`
• E`rlpyt_A2C_Atari(pong)`
• E`rlpyt_PPO_Atari(pong)`

Run Experiments

julia> ] add ReinforcementLearning

julia> using ReinforcementLearning

julia> run(E`JuliaRL_BasicDQN_CartPole`)

julia> ] add ArcadeLearningEnvironment

julia> using ArcadeLearningEnvironment

julia> run(E`rlpyt_PPO_Atari(pong)`)  # the Atari environment is provided in ArcadeLearningEnvironment, so we need to install it first

Notes:

• Experiments on CartPole usually run faster with CPU only due to the overhead of sending data between CPU and GPU.
• It shouldn't surprise you that our experiments on CartPole are much faster than those written in Python. The secret is that our environment is written in Julia!
• Remember to set JULIA_NUM_THREADS to enable multi-threading when using algorithms like A2C and PPO.
• Experiments on Atari (OpenSpiel, SnakeGame, GridWorlds) are only available after you have ArcadeLearningEnvironment.jl (OpenSpiel.jl, SnakeGame.jl, GridWorlds.jl) installed and using ArcadeLearningEnvironment (using OpenSpiel, using SnakeGame, import GridWorlds).

Speed

• Different configurations might affect the performance a lot. According to our tests, our implementations are generally comparable to those written in PyTorch or TensorFlow with the same configuration (sometimes we are significantly faster).

The following data are collected from experiments on Intel(R) Xeon(R) W-2123 CPU @ 3.60GHz with a GPU card of RTX 2080ti.

Experiment	FPS	Notes
E`Dopamine_DQN_Atari(pong)`	~210	Use the same config of dqn.gin in google/dopamine
E`Dopamine_Rainbow_Atari(pong)`	~171	Use the same config of rainbow.gin in google/dopamine
E`Dopamine_IQN_Atari(pong)`	~162	Use the same config of implicit_quantile.gin in google/dopamine
E`rlpyt_A2C_Atari(pong)`	~768	Use the same default parameters of A2C in rlpyt with 4 threads
E`rlpyt_PPO_Atari(pong)`	~711	Use the same default parameters of PPO in rlpyt with 4 threads