lars12llt Goto Github PK

adversarial-surprise

Explore and Control with Adversarial Surprise

brax

Massively parallel rigidbody physics simulation on accelerator hardware.

code-for-paper

collaq

A code implementation for our arXiv paper "Multi-agent Adhoc Team Play using Decompositional Q function"

dice_rl

distributedrl

A framework for easy prototyping of distributed reinforcement learning algorithms

dqn_zoo

DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.

dreamerv2

Mastering Atari with Discrete World Models

driml

Code for Deep Reinforcement and InfoMax Learning (Neurips 2020)

efficientzero

Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

google-research

Google Research

h-baselines

A repository of high-performing hierarchical reinforcement learning models and algorithms.

hopfield-layers

Hopfield Networks is All You Need

ibsgd

jax-rl

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

level-replay

This code implements Prioritized Level Replay, a method for sampling training levels for reinforcement learning agents that exploits the fact that not all levels are equally useful for agents to learn from during training.

mrcl

Code for the NeurIPS19 paper "Meta-Learning Representations for Continual Learning"

muzero

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.

muzero-general

MuZero

ntk

Code for experiments in my blog post on the Neural Tangent Kernel: https://rajatvd.github.io/NTK

optimalrepresentationrl

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

procgen-competition

Sample efficiency and generalisation in reinforcement learning using procedural generation.

pytorch-a2c-ppo-acktr-gail

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

pytorch-ls-dqn

PyTorch implementation of Least-Squares DQN

pytorch_sac_ae

PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)

rad

RAD: Reinforcement Learning with Augmented Data

re3

RE3: State Entropy Maximization with Random Encoders for Efficient Exploration

reinforcement-learning-algorithms

This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO. (More algorithms are still in progress)

rlpyt

Reinforcement Learning in PyTorch

seed_rl

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.