Status: Under development (expect bug fixes and huge updates)

ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

ShinRL is an open-source JAX library specialized for the evaluation of reinforcement learning (RL) algorithms from both theoretical and practical perspectives. Please take a look at the paper for details. Try ShinRL at experiments/QuickStart.ipynb.

QuickStart

import gym
from shinrl import DiscreteViSolver
import matplotlib.pyplot as plt

# make an env & a config
env = gym.make("ShinPendulum-v0")
config = DiscreteViSolver.DefaultConfig(explore="eps_greedy", approx="nn", steps_per_epoch=10000)

# make & run a solver
mixins = DiscreteViSolver.make_mixins(env, config)
dqn_solver = DiscreteViSolver.factory(env, config, mixins)
dqn_solver.run()

# plot performance
returns = dqn_solver.scalars["Return"]
plt.plot(returns["x"], returns["y"])

# plot learned q-values  (action == 0)
q0 = dqn_solver.data["Q"][:, 0]
env.plot_S(q0, title="Learned")

⚡ Key Modules

🔬 ShinEnv for Oracle Analysis

• ShinEnv provides small environments with oracle methods that can compute exact quantities.
• Some environments support continuous action space and image observation:
• See the tutorial for details: experiments/Tutorials/ShinEnvTutorial.ipynb.

Environment	Discrete action	Continuous action	Image Observation	Tuple Observation
ShinMaze	✔️	❌	❌	✔️
ShinMountainCar-v0	✔️	✔️	✔️	✔️
ShinPendulum-v0	✔️	✔️	✔️	✔️
ShinCartPole-v0	✔️	✔️	❌	✔️

🏭 Flexible Solver by MixIn

• A Solver solves an environment with specified algorithms.
• A "mixin" is a class which defines and implements a single feature. ShinRL's solvers are instantiated by mixing some mixins.
• See the tutorial for details: experiments/Tutorials/SolverTutorial.ipynb.

Implemented Popular Algorithms

• The table bellow lists the implemented popular algorithms.
• Note that it does not list all the implemented algorithms (e.g., DDP ¹ version of the DQN algorithm). See make_mixin functions of solvers for implemented variants.
• Note that the implemented algorithms may differ from the original implementation for simplicity (e.g., Discrete SAC). See source code of solvers for details.

Algorithm	Solver	Configuration	Type 1
Value Iteration (VI)	DiscreteViSolver	approx == "tabular" & explore == "oracle"	TDP
Policy Iteration (PI)	DiscretePiSolver	approx == "tabular" & explore == "oracle"	TDP
Conservative Value Iteration (CVI)	DiscreteViSolver	approx == "tabular" & explore == "oracle & er_coef != 0 & kl_coef != 0"	TDP
Tabular Q Learning	DiscreteViSolver	approx == "tabular" & explore != "oracle"	TRL
SARSA	DiscretePiSolver	approx == "tabular" & explore != "oracle" & eps_decay_target_pol > 0	TRL
Deep Q Network (DQN)	DiscreteViSolver	approx == "nn" & explore != "oracle"	DRL
Soft DQN	DiscreteViSolver	approx == "nn" & explore != "oracle" & er_coef != 0	DRL
Munchausen-DQN	DiscreteViSolver	approx == "nn" & explore != "oracle" & er_coef != 0 & kl_coef != 0	DRL
Double-DQN	DiscreteViSolver	approx == "nn" & explore != "oracle" & use_double_q == True	DRL
Discrete Soft Actor Critic	DiscretePiSolver	approx == "nn" & explore != "oracle" & er_coef != 0	DRL
Deep Deterministic Policy Gradient (DDPG)	ContinuousDdpgSolver	approx == "nn" & explore != "oracle"	DRL

1 Algorithm Type:

• TDP (approx=="tabular" & explore=="oracle"): Tabular Dynamic Programming algorithms. No exploration & no approximation & the complete specification about the MDP is given.
• TRL (approx=="tabular" & explore!="oracle"): Tabular Reinforcement Learning algorithms. No approximation & the dynamics and the reward functions are unknown.
• DDP (approx=="nn" & explore=="oracle"): Deep Dynamic Programming algorithms. It is the same as TDP, except that neural networks approximate computed values.
• DRL (approx=="nn" & explore!="oracle"): Deep Reinforcement Learning algorithms. It is the same as TRL, except that neural networks approximate computed values.

Installation

git clone git@github.com:omron-sinicx/ShinRL.git
cd ShinRL
pip install -e .

Test

cd ShinRL
make test

Format

cd ShinRL
make format

Docker

cd ShinRL
docker-compose up

Citation

# Neurips DRL WS 2021 version (pytorch branch)
@inproceedings{toshinori2021shinrl,
    author = {Kitamura, Toshinori and Yonetani, Ryo},
    title = {ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives},
    year = {2021},
    booktitle = {Proceedings of the NeurIPS Deep RL Workshop},
}

# Arxiv version (commit 2d3da)
@article{toshinori2021shinrlArxiv,
    author = {Kitamura, Toshinori and Yonetani, Ryo},
    title = {ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives},
    year = {2021},
    url = {https://arxiv.org/abs/2112.04123},
    journal={arXiv preprint arXiv:2112.04123},
}