eugenevinitsky / robust_RL_multi_adversary
We investigate the effect of populations on finding good solutions to the robust MDP
☆28Updated 4 years ago
Alternatives and similar repositories for robust_RL_multi_adversary:
Users that are interested in robust_RL_multi_adversary are comparing it to the libraries listed below
- Safe Model-based Reinforcement Learning with Robust Cross-Entropy Method☆66Updated 2 years ago
- ☆53Updated last year
- Codes accompanying the paper "DOP: Off-Policy Multi-Agent Decomposed Policy Gradients" (ICLR 2021, https://arxiv.org/abs/2007.12322)☆52Updated 2 years ago
- Implementation of Tactical Optimistic and Pessimistic value estimation☆25Updated last year
- ☆26Updated 2 years ago
- An open-source framework to benchmark and assess safety specifications of Reinforcement Learning problems.☆67Updated last year
- ☆55Updated 2 years ago
- Code for Latent Action Space for Offline Reinforcement Learning [CoRL 2020]☆52Updated 3 years ago
- Implementation of the skill discovery algorithm described in ICLR submission "Option Discovery using Deep Skill Chaining"☆28Updated 5 years ago
- Code accompanying the paper "Action Robust Reinforcement Learning and Applications in Continuous Control" https://arxiv.org/abs/1901.0918…☆43Updated 6 years ago
- DecentralizedLearning☆24Updated 2 years ago
- Code for the NeurIPS 2021 paper "Safe Reinforcement Learning by Imagining the Near Future"☆44Updated 3 years ago
- Implementations of SAILR, PDO, and CSC☆32Updated 9 months ago
- ☆31Updated 4 years ago
- ☆17Updated 3 years ago
- Implementation of ICML2020 paper <Bidirectional Model-based Policy Optimization>☆23Updated 2 years ago
- Learning Off-Policy with Online Planning [CoRL 2021 Best Paper Finalist]☆37Updated 2 years ago
- Gym environments modified with adversarial agents☆36Updated 8 years ago
- ☆21Updated last year
- Offline Risk-Averse Actor-Critic (O-RAAC). A model-free RL algorithm for risk-averse RL in a fully offline setting☆35Updated 4 years ago
- The official repository of Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration" (AAMAS 2022)☆27Updated 3 years ago
- ☆18Updated 2 years ago
- Safe Policy Improvement with Baseline Bootstrapping☆26Updated 5 years ago
- PyTorch IMPALA implementation☆26Updated 5 years ago
- Inverse Reinforcement Learning via State Marginal Matching, CoRL 2020☆45Updated last year
- PyTorch implementation of our paper Reinforcement Learning with Random Delays (ICLR 2020)☆40Updated 2 years ago
- Simple maze environments using mujoco-py☆54Updated last year
- on-policy optimization baselines for deep reinforcement learning☆30Updated 5 years ago
- Code for demonstration example-task in RUDDER blog☆23Updated 4 years ago
- ☆98Updated 2 years ago