Robust Reinforcement Learning

Robust reinforcement learning (RL) focuses on developing RL agents that perform well even when faced with uncertainties in the environment, such as noisy observations, model mismatches, or adversarial attacks. Current research emphasizes techniques like adversarial training, distributionally robust optimization, and the use of pessimistic models to improve robustness, often incorporating actor-critic algorithms, model-based approaches, and Lipschitz-constrained policy networks. This field is crucial for deploying RL agents in real-world settings where perfect knowledge of the environment is unrealistic, impacting areas like robotics, autonomous systems, and finance by enabling safer and more reliable AI.

Papers

November 18, 2024

November 15, 2024

Guiding Reinforcement Learning Using Uncertainty-Aware Large Language Models
Maryam Shoaeinaeini, Brent Harrison
Reinforcement Learning Full Model Robust Reinforcement Learning Fine Tuned LLM Overconfidence Problem

October 16, 2024

Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving
Sihao Wu, Jiaxu Liu, Xiangyu Yin, Guangliang Cheng, Meng Fang, Xingyu Zhao, Xinping Yi, Xiaowei Huang
Reinforcement Learning Autonomous Driving LLM Agent Robust Reinforcement Learning Data Synthesis Policy Adaptation

September 16, 2024

Robust Reinforcement Learning with Dynamic Distortion Risk Measures
Anthony Coache, Sebastian Jaimungal
Reinforcement Learning Actor Critic Algorithm Robust Reinforcement Learning Distortion Metric Risk Measure Risk Aware Reinforcement Learning

August 31, 2024

Robust off-policy Reinforcement Learning via Soft Constrained Adversary
Kosuke Nakanishi, Akihiro Kubo, Yuji Yasui, Shin Ishii
Robust Learning Policy Reinforcement Learning Robust Reinforcement Learning Adversarial Reinforcement Learning Soft Constraint

August 23, 2024

SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning
Zhongjian Qiao, Jiafei Lyu, Kechen Jiao, Qi Liu, Xiu Li
Offline Reinforcement Learning Robust Reinforcement Learning Model Based Offline Reinforcement Learning Accurate Uncertainty

August 17, 2024

Training Verifiably Robust Agents Using Set-Based Reinforcement Learning
Manuel Wendl, Lukas Koller, Tobias Ladner, Matthias Althoff
Reinforcement Learning Adversarial Attack Action Space Reachability Analysis Robust Reinforcement Learning Set Valued Robust Agent

July 31, 2024

On the Perturbed States for Transformed Input-robust Reinforcement Learning
Tung M. Luu, Haeyong Kang, Tri Ton, Thanh Nguyen, Chang D. Yoo
Reinforcement Learning Robust Reinforcement Learning Robust Agent State Perturbation

June 22, 2024

Distributionally Robust Constrained Reinforcement Learning under Strong Duality
Zhengfei Zhang, Kishan Panaganti, Laixi Shi, Yanan Sui, Adam Wierman, Yisong Yue
Convergence Guarantee Robust Reinforcement Learning Distributionally Robust Distributional Robustness Strong Duality Natural Distribution Shift

June 21, 2024

Robust Reinforcement Learning from Corrupted Human Feedback
Alexander Bukharin, Ilgee Hong, Haoming Jiang, Zichong Li, Qingru Zhang, Zixuan Zhang, Tuo Zhao
Reinforcement Learning Direct Preference Optimization Preference Optimization Reinforcement Learning From Human Feedback Robust Reinforcement Learning Robust Reward

June 14, 2024

Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model
Siemen Herremans, Ali Anwar, Siegfried Mercelis
Reinforcement Learning Markov Decision Process Robust Reinforcement Learning Auxiliary Adversarial

June 12, 2024

June 6, 2024

Bootstrapping Expectiles in Reinforcement Learning
Pierre Clavier, Emmanuel Rachelson, Erwan Le Pennec, Matthieu Geist
Reinforcement Learning Robust Reinforcement Learning Double Q Learning Critic Regularization

May 23, 2024

MuDreamer: Learning Predictive World Models without Reconstruction
Maxime Burchi, Radu Timofte
Full State Reconstruction Robust Reinforcement Learning DeepMind Control Suite Predictive World Model

May 19, 2024

On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
Deep Reinforcement Learning Robust Policy Robust Reinforcement Learning Policy Parameterization Policy Network Lipschitz Network

May 3, 2024

Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach
Anton Plaksin, Vitaly Kalev
New Framework Multi Agent Reinforcement Learning Constructive Approach Robust Reinforcement Learning Differential Game Centralized Reinforcement Learning

April 29, 2024

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman
Reinforcement Learning Multi Agent Reinforcement Learning Human Face Markov Game Robust Reinforcement Learning Uncertain Environment Equilibrium Approximators

April 22, 2024

Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Ning Yang, Shuo Chen, Haijun Zhang, Randall Berry
Reinforcement Learning Extreme Edge Robust Reinforcement Learning Mobile Edge Computing Edge Network Research Trend