- Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning, Paper (To be released), Code, (Accepted by ICML 2024)
- Towards Robust Offline Reinforcement Learning under Diverse Data Corruption, Paper, Code, (Accepted by ICLR 2024)
- Robust Offline Reinforcement Learning with Heavy-Tailed Rewards, Paper, Code, (Accepted by AISTATS 2024)
- Causal Counterfactuals for Improving the Robustness of Reinforcement Learning, Paper, Code, (Accepted by AAMAS 2023)
- RAMRL: Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning, Paper, Code, (Accepted by MOST 2023)
- Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum, Paper, Code, (Accepted by ICML 2022)
- Robust offline Reinforcement Learning via Conservative Smoothing, Paper, Code, (Accepted by NeurIPS 2022)
- Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning, Paper, Code, (Accepted by NeurIPS 2022)
- Robust Reinforcement Learning using Offline Data, Paper, Code, (Accepted by NeurIPS 2022)
- CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing, Paper, Code, (Accepted by ICLR 2022)
- COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks, Paper, Code, (Accepted by ICLR 2022)
- Robust Risk-Aware Reinforcement Learning, Paper, Code, (Accepted by SIAM Journal on Financial Mathematics, 2022)
- Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning, Paper, Code, (Accepted by NeurIPS 2022)
- Robust Deep Reinforcement Learning through Adversarial Loss, Paper, Code, (Accepted by NeurIPS 2021)
- Robust Reinforcement Learning with Alternating Training of Learned Adversaries, Paper, Code, (Accepted by ICLR 2021)
- Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning, Paper, Code, (Accepted by ICRA 2021)
- Robust Reinforcement Learning Under Minimax Regret for Green Security, Paper, Code, (Accepted by UAI 2021)
- Robust Reinforcement Learning via Adversarial training with Langevin Dynamics, Paper, Code, (Accepted by NeurIPS 2020)
- Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations, Paper, Code, (Accepted by NeurIPS 2020)
- Action Robust Reinforcement Learning and Applications in Continuous Control, Paper, Code, (Accepted by ICML 2019)
- Robust Domain Randomization for Reinforcement Learning, Paper, Code, (Arxiv, 2019)
- Robust Adversarial Reinforcement Learning, Paper, Code, (Accepted by ICML 2015)