Stars
This code corresponds to simulation environments used as part of the MimicGen project.
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning
A python interface for training Reinforcement Learning bots to battle on pokemon showdown
JAX-accelerated Meta-Reinforcement Learning Environments Inspired by XLand and MiniGrid 🏎️
A collection of recent papers on building autonomous agent. Two topics included: RL-based / LLM-based agents.
a simple and scalable agent for training adaptive policies with sequence-based RL
Challenging Memory-based Deep Reinforcement Learning Agents
CVPR and NeurIPS poster examples and templates. May we have in-person poster session soon!
Retro games for Reinforcement Learning
Really Fast End-to-End Jax RL Implementations
Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."
Benchmarking the Spectrum of Agent Capabilities
Deep Transformer Q-Networks for Partially Observable Reinforcement Learning
robomimic: A Modular Framework for Robot Learning from Demonstration
Implementation of VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning - Zintgraf et al. (ICLR 2020)
PyTorch implementation of the Option-Critic framework, Harb et al. 2016
Fast and memory-efficient exact attention
A beautiful, simple, clean, and responsive Jekyll theme for academics
[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.
Implementation of https://srush.github.io/annotated-s4
A customizable framework to create maze and gridworld environments
Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
Structured state space sequence models
Official codebase for Manipulation Primitive-augmented reinforcement Learning (MAPLE)
Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation (BUDS)