Google Research

A course in reinforcement learning in the wild

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

Massively parallel rigidbody physics simulation on accelerator hardware.

"Deep Generative Modeling": Introductory Examples

JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.

《Machine Learning: A Probabilistic Perspective》（Kevin P. Murphy）中文翻译和书中算法的Python实现。

PyTorch implementation of Soft Actor-Critic (SAC)

RAD: Reinforcement Learning with Augmented Data

Code for experiments in my blog post on the Neural Tangent Kernel: https://eigentales.com/NTK

A clean implementation of MuZero and AlphaZero following the AlphaZero General framework. Train and Pit both algorithms against each other, and investigate reliability of learned MuZero MDP models.

DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN

RL experiments

RE3: State Entropy Maximization with Random Encoders for Efficient Exploration

codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

Code for the paper "Batch size invariance for policy optimization"

Code repository for the research project "You Play Ball, I Play Ball: Bayesian Multi-Agent Reinforcement Learning for Slime Volleyball", won 1st Prize at 17th STePS.

Prioritized Sequence Experience Replay

An implementation in PyTorch of the paper "A Geometric Perspective on Optimal Representations for Reinforcement Learning" by Bellemare et al

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

CS182 Final Project - aimed to improve generalization of common reinforcement learning algorithms (e.g. PPO w/ A2C) on the ProcGen suite of environments

Research repo looking at using automatic attention based methods to speed up Contrastive Learning methods in reinforcement learning environments