Ueva / BaRL-SimpleOptions
A Python package that provides a simple framework for working with Options in Reinforcement Learning.
☆21Updated 2 months ago
Alternatives and similar repositories for BaRL-SimpleOptions:
Users that are interested in BaRL-SimpleOptions are comparing it to the libraries listed below
- A Tensorflow implementation of the Option-Critic Architecture☆72Updated 7 years ago
- Github repo for HIDIO: Hierarchical Reinforcement Learning by Discovering Intrinsic Options☆45Updated 3 years ago
- Hierarchical Self-Play☆21Updated 6 years ago
- JAX implementation of deep RL agents with resets from the paper "The Primacy Bias in Deep Reinforcement Learning"☆100Updated 2 years ago
- The Starcraft Multi-Agent challenge lite☆42Updated 7 months ago
- We investigate the effect of populations on finding good solutions to the robust MDP☆28Updated 4 years ago
- Playing Mountain-Car without reward engineering, by combining DQN and Random Network Distillation (RND)☆40Updated 6 years ago
- ☆53Updated last year
- Source code for "A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning" (ICML 2021)☆31Updated 2 years ago
- PyTorch Implementation of FeUdal Networks for Hierarchical Reinforcement Learning (FuNs), Vezhnevets et al. 2017.☆39Updated 4 years ago
- Implementation of the Option-Critic Architecture☆39Updated 6 years ago
- Gridworld for MARL experiments☆139Updated 4 years ago
- ☆49Updated 3 years ago
- The official repository of Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration" (AAMAS 2022)☆27Updated 3 years ago
- ☆83Updated 4 years ago
- Implementation of the skill discovery algorithm described in ICLR submission "Option Discovery using Deep Skill Chaining"☆28Updated 5 years ago
- ☆75Updated 10 months ago
- PyTorch implementation of Never Give Up: Learning Directed Exploration Strategies☆57Updated 4 years ago
- Codes accompanying the paper "DOP: Off-Policy Multi-Agent Decomposed Policy Gradients" (ICLR 2021, https://arxiv.org/abs/2007.12322)☆52Updated 2 years ago
- This repository contains the implementation for the paper - Exploration via Hierarchical Meta Reinforcement Learning.☆60Updated 5 years ago
- Submission for MAVEN: Multi-Agent Variational Exploration☆57Updated 3 years ago
- Project on Successor Features in Deep Reinforcement Learning and Transfer Learning☆24Updated 7 years ago
- PyTorch implementation of our paper Reinforcement Learning with Random Delays (ICLR 2020)☆40Updated 2 years ago
- Code accompanying HAAR paper, NeurIPS 2019 - Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards☆32Updated 2 years ago
- Random network distillation on Montezuma's Revenge and Super Mario Bros.☆49Updated 2 years ago
- Code for Multi-Agent Common Knowledge Reinforcement Learning (NeurIPS 2019)☆33Updated 5 years ago
- Code for "Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning"☆35Updated 3 years ago
- Experiment code for testing effect of various action space transformations in reinforcement learning☆30Updated 4 years ago
- Implementation of Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (Heinrich and Silver, 2016)☆46Updated 6 years ago
- N-Layered FeUdal Networks based on FeUdal Networks adapted to suit PySC2 observations☆16Updated 5 years ago