Blank Shuo's repositories
awesome-offline-rl
An index of algorithms for offline reinforcement learning (offline-rl)
BCQ
Author's PyTorch implementation of BCQ for continuous and discrete actions
CORL
High-quality single-file implementations of SOTA Offline RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC
CQL
Code for conservative Q-learning
CQL-1
PyTorch implementation of the Offline Reinforcement Learning algorithm CQL. Includes the versions DQN-CQL and SAC-CQL for discrete and continuous action spaces.
CQL-2
Conservative Q Learning on top of SAC
d3rlpy
An offline deep reinforcement learning library
D4RL
A benchmark for offline reinforcement learning.
Deep-reinforcement-learning-with-pytorch
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
DI-engine
OpenDILab Decision AI Engine
mjrl
Reinforcement learning algorithms for MuJoCo tasks
Offline-Online-RL
Code for OFFLINE-ONLINE REINFORCEMENT LEARNING: EXTENDING BATCH AND ONLINE RL
offline_rl
Pytorch implementation of state-of-the-art offline reinforcement learning algorithms.
Papers-of-Offline-RL
Related papers for offline reforcement learning (we mainly focus on representation and sequence modeling and conventional offline RL)
pytorch-soft-actor-critic
PyTorch implementation of soft actor critic
pytorch_sac
PyTorch implementation of Soft Actor-Critic (SAC)
rl-plotter
:sparkles: A plotter for reinforcement learning (RL)
RL-Unplugged-tfds-
This repository contains implementations and illustrative code to accompany DeepMind publications
rlkit
Collection of reinforcement learning algorithms
sac
Soft Actor-Critic
TD3
Author's PyTorch implementation of TD3 for OpenAI gym tasks
TD3_BC
Author's PyTorch implementation of TD3+BC, a simple variant of TD3 for offline RL
td3_bc_jax
Direct port of TD3_BC to JAX using Haiku and optax.
v-d4rl
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
YOLOAir
🔥🔥🔥YOLOv7, YOLOv5, YOLOv4, Transformer, YOLOX, YOLOR, YOLOv3 and Improved-YOLOv5... Support to improve backbone, head, loss, IoU, NMS and other modules