model-based-reinforcement-learning

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

A curated list of awesome model based RL resources (continually updated)

Library for Model Based RL

Code to reproduce the experiments in Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation (MEEE).

Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"

DI-engine docs (Chinese and English)

Unofficial Pytorch code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"

Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments

Official repository for "iVideoGPT: Interactive VideoGPTs are Scalable World Models" (NeurIPS 2024), https://arxiv.org/abs/2405.15223

Model-based Reinforcement Learning Framework

(Experimental, a lot of bugs) Automatic fingering generator for piano scores, determining optimal fingering using Model-Based Reinforcement Learning, written in the Julia language.

Code release for Efficient Planning in a Compact Latent Action Space (ICLR2023) https://arxiv.org/abs/2208.10291.

Implementation of the new SOTA for model based RL, from the paper "Improving Transformer World Models for Data-Efficient RL", in Pytorch

A curated list of awesome Model-based reinforcement learning resources

Deep active inference agents using Monte-Carlo methods

Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing

Code for "World Model as a Graph: Learning Latent Landmarks for Planning" (ICML 2021 Long Presentation)

Recall to Imagine, a model-based RL algorithm with superhuman memory. Oral (1.2%) @ ICLR 2024

An implementation of MuZero in JAX.