ChuaCheowHuan/sagemaker_Ray_RLlib_custom_env

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2
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Python
License
MIT License
Created about 5 years ago
Updated about 2 years ago

ChuaCheowHuan/sagemaker_Ray_RLlib_custom_env

ChuaCheowHuan

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Sample setup for custom reinforcement learning environment in Sagemaker. This example uses Proximal Policy Optimization with Ray (RLlib).

gym-continuousDoubleAuction

A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.

Jupyter Notebook

reinforcement_learning

My reproduction of various reinforcement learning algorithms (DQN variants, A3C, DPPO, RND with PPO) in Tensorflow.

Jupyter Notebook

PBT_MARL_watered_down

My attempt to reproduce a water down version of PBT (Population based training) for MARL (Multi-agent reinforcement learning) using DDPPO (Decentralized & distributed proximal policy optimization) from ray[rllib].

Jupyter Notebook

web_app_DPDTH

Sample CI/CD devops setup of Django & Postgres web app with Docker, Travis & Heroku.

bayesian_ML

Bayesian based machine learning implementations (GMM, VAE & conditional VAE).

Jupyter Notebook