An out-of-the-box GUI tool for offline deep reinforcement learning

Learning representations for RL in Healthcare under a POMDP assumption

Direct port of TD3_BC to JAX using Haiku and optax.

PyTorch implementation of the implicit Q-learning algorithm (IQL)

The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation (NeurIPS 2021) by Alex J. Chan, Ioana Bica, Alihan Huyuk, Daniel Jarrett, and Mihaela van der Schaar.

ExORL: Exploratory Data for Offline Reinforcement Learning

Code for Continuous Doubly Constrained Batch Reinforcement Learning, NeurIPS 2021.

Codes accompanying the paper "Offline Reinforcement Learning with Value-Based Episodic Memory" (ICLR 2022 https://arxiv.org/abs/2110.09796)

PyTorch Implementation of Offline Reinforcement Learning algorithms

Package for recording Transitions in OpenAI Gym Environments.

Code for the paper "Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters", ICML 2022

Pytorch implementation of state-of-the-art offline reinforcement learning algorithms.

[MLHC 2021] Model Selection for Offline RL: Practical Considerations for Healthcare Settings. https://arxiv.org/abs/2107.11003

A large-scale multi-modal pre-trained model

Conservative Q learning in Jax

Extreme Q-Learning: Max Entropy RL without Entropy

Code to reproduce experiments from "User-Interactive Offline Reinforcement Learning" (ICLR 2023)

Neural Laplace Control for Continuous-time Delayed Systems - an offline RL method combining Neural Laplace dynamics model and MPC planner to achieve near-expert policy performance in environments with irregular time intervals and an unknown constant delay.

Offline to Online RL: AWAC & IQL PyTorch Implementation

Code for Undergrad Final Year Project “Offline Risk-Averse Actor-Critic with Curriculum Learning”