Dhruv Sreenivas's repositories
byol-offline
Bootstrap your own latent (BYOL) methods in offline reinforcement learning
jax_sandbox
JAX messaround
acme
A library of reinforcement learning components and agents.
amp_extensions
Extension of AMP framework (https://github.com/xbpeng/DeepMimic) to include gym environment. Also, adapted code from MILO (https://github.com/jdchang1/milo) to test on AMP framework.
DeepMimic
Motion imitation with deep reinforcement learning.
dm_control
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
dots-and-boxes
Testing various AI methods for the game Dots & Boxes
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.
dril
Disagreement-Regularized Imitation Learning
gym-simplifiedtetris
🟥 Simplified Tetris environments compliant with OpenAI Gym's API
homework_fall2020
Assignments for Berkeley CS 285: Deep Reinforcement Learning (Fall 2020)
IQ-Learn
(NeurIPS '21 Spotlight) IQ-Learn: Inverse Q-Learning for Imitation
IsaacGymEnvs
Isaac Gym Reinforcement Learning Environments
lightATAC-rlhf
A lightweight reimplementation of Adversarially Trained Actor Critic
OfflineRL-Kit
An elegant PyTorch offline reinforcement learning library for researchers.
pillbox
Contains implementation of AdVIL, AdRIL, and DAeQuIL algorithms from the ICML '21 Paper Of Moments and Matching.
pytorch-a2c-trpo-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_sac
PyTorch implementation of Soft Actor-Critic (SAC)
rl-trained-agents
A collection of pre-trained RL agents using Stable Baselines3
sqil-atari
Implementation of SQIL for Atari games
TD3_BC
Author's PyTorch implementation of TD3+BC, a simple variant of TD3 for offline RL
v-d4rl
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations