clementbernardd / Count-Based-ExplorationLinks
Our version of #Exploration: A Study of Count-Based Explorationfor Deep Reinforcement Learning for a class project
☆16Updated 4 years ago
Alternatives and similar repositories for Count-Based-Exploration
Users that are interested in Count-Based-Exploration are comparing it to the libraries listed below
Sorting:
- Implementation of Population-Guided Parallel Policy Search for Reinforcement Learning☆22Updated 5 years ago
- Code from the paper "Effective Diversity in Population Based Reinforcement Learning", presented as a spotlight at NeurIPS 2020. This is t…☆44Updated 5 years ago
- Evolution-based Soft Actor-Critic (ESAC)☆42Updated last year
- Open source demo for the paper Learning to Score Behaviors for Guided Policy Optimization☆24Updated 5 years ago
- We investigate the effect of populations on finding good solutions to the robust MDP☆28Updated 4 years ago
- Code accompanying the paper "Action Robust Reinforcement Learning and Applications in Continuous Control" https://arxiv.org/abs/1901.0918…☆48Updated 6 years ago
- The official repository of Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration" (AAMAS 2022)☆27Updated 3 years ago
- Codes accompanying the paper "DOP: Off-Policy Multi-Agent Decomposed Policy Gradients" (ICLR 2021, https://arxiv.org/abs/2007.12322)☆51Updated 3 years ago
- (Experimental) Inverse reinforcement learning from trajectories generated by multiple agents with different (but correlated) rewards☆27Updated 6 years ago
- Code accompanying NeurIPS 2019 paper: "Distributional Policy Optimization - An Alternative Approach for Continuous Control"☆22Updated 5 years ago
- Combining Evolutionary Algorithms and deep RL in various ways☆107Updated 5 years ago
- Code for "Proximal Distilled Evolutionary Reinforcement Learning", accepted at AAAI 2020☆55Updated last year
- Experiment code for testing effect of various action space transformations in reinforcement learning☆30Updated 5 years ago
- Negative Update Intervals in Multi-Agent Deep Reinforcement Learning☆34Updated 6 years ago
- ☆20Updated 2 years ago
- PyTorch Implementation of FeUdal Networks for Hierarchical Reinforcement Learning (FuNs), Vezhnevets et al. 2017.☆41Updated 5 years ago
- Implementation of the Option-Critic Architecture☆40Updated 7 years ago
- Code for "Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning"☆36Updated 4 years ago
- Prioritized Sequence Experience Replay☆10Updated 4 years ago
- ☆49Updated 4 years ago
- ☆78Updated last year
- Source code for "A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning" (ICML 2021)☆33Updated 3 years ago
- PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning☆51Updated 4 years ago
- implementation of "Evolution Strategies as a Scalable Alternative to Reinforcement Learning" OpenAI paper☆20Updated 4 years ago
- Value-Decomposition Multi-Agent Actor-Critics☆41Updated 3 years ago
- Code for the paper "Meta-Q-Learning"( ICLR 2020)☆106Updated 3 years ago
- Explorer is a PyTorch reinforcement learning framework for exploring new ideas.☆97Updated 5 months ago
- Soft Actor-Critic with advanced features☆50Updated last month
- Implementation of the Model-Based Meta-Policy-Optimization (MB-MPO) algorithm☆44Updated 7 years ago
- Safe Reinforcement Learning in Constrained Markov Decision Processes☆61Updated 5 years ago