RL Agents
Jax | Flux | PyTorch

A collection of Reinforcement Learning (RL) Methods I have implemented in jax/flax, flux and pytorch with particular effort put into readability and reproducibility.

Python

Requirements For Jax

• Python >= 3.8
• jax

Installation

$ git clone https://github.com/BeeGass/Agents.git

Usage

$ cd Agents/agents-jax
$ python main.py 

Requirements For PyTorch

• PyTorch >= 1.10

Usage

$ cd Agents/agents-pytorch
$ python main.py 

Julia

Requirements For Flux

• TODO
• TODO

Usage

$ cd Agents/agents-flux
$ # TBA 

---

Config File Template

TBA

Weights And Biases Integration

TBA

---

Preliminary RL Implementations

Model	NumPy/Vanilla	Jax/Flax	Flux	Config	Paper
Policy Evaluation	☑	☐	☐	☐	DS595-RL-Projects
Policy Improvement	☑	☐	☐	☐	DS595-RL-Projects
Policy Iteration	☑	☐	☐	☐	DS595-RL-Projects
Value Iteration	☑	☐	☐	☐	DS595-RL-Projects
On-policy first visit Monte-Carlo prediction	☑	☐	☐	☐	DS595-RL-Projects
On-policy first visit Monte-Carlo control	☑	☐	☐	☐	DS595-RL-Projects
Sarsa (on-policy TD control)	☑	☐	☐	☐	DS595-RL-Projects
Q-learing (off-policy TD control)	☑	☐	☐	☐	DS595-RL-Projects

---

Off-Policy Results

Model	PyTorch	Jax/Flax	Flux	Config	Paper
DQN	☐	☐	☐	☐	Link
DDPG	☐	☐	☐	☐	Link
DRQN	☐	☐	☐	☐	Link
Dueling-DQN	☐	☐	☐	☐	Link
Double-DQN	☐	☐	☐	☐	Link
PER	☐	☐	☐	☐	Link
Rainbow	☐	☐	☐	☐	Link

Policy Results

Model	PyTorch	Jax/Flax	Flux	Config	Paper
PPO	☐	☐	☐	☐	Link
TRPO	☐	☐	☐	☐	Link
SAC	☐	☐	☐	☐	Link
A2C	☐	☐	☐	☐	Link
A3C	☐	☐	☐	☐	Link
TD3	☐	☐	☐	☐	Link

Fun Stuff

Model	PyTorch	Jax/Flax	Flux	Config	Paper
World Models	☐	☐	☐	☐	Link
Dream to Control	☐	☐	☐	☐	Link
Dream to Control v2	☐	☐	☐	☐	Link

Citation

@software{Gass_Agents_2021,
  author = {Gass, B.A., Gass, B.A.},
  doi = {10.5281/zenodo.1234},
  month = {12},
  title = {{Agents}},
  url = {https://github.com/BeeGass/Agents},
  version = {1.0.0},
  year = {2021}
}