This study tackles the challenges of adversarial corruption in model-based reinforcement
learning (RL), where the transition dynamics can be corrupted by an adversary. Existing …

We develop a model selection approach to tackle reinforcement learning with adversarial
corruption in both transition and reward. For finite-horizon tabular MDPs, without prior …

Offline reinforcement learning (RL) addresses the challenge of expensive and high-risk data
exploration inherent in RL by pre-training policies on vast amounts of offline data, enabling …

We study the problem of robust reinforcement learning under adversarial corruption on both
rewards and transitions. Our attack model assumes an\textit {adaptive} adversary who can …

In light of the burgeoning success of reinforcement learning (RL) in diverse real-world
applications, considerable focus has been directed towards ensuring RL policies are robust …

As reinforcement learning (RL) has achieved great success and been even adopted in
safety-critical domains such as autonomous vehicles, a range of empirical studies have …

We study episodic reinforcement learning under unknown adversarial corruptions in both
the rewards and the transition probabilities of the underlying system. We propose new …

This note clarifies some confusions (and perhaps throws out more) around model-based
reinforcement learning and their theoretical understanding in the context of deep RL. Main …

We study data corruption robustness for reinforcement learning with human feedback
(RLHF) in an offline setting. Given an offline dataset of pairs of trajectories along with …

In real-world tasks, reinforcement learning (RL) agents frequently encounter situations that
are not present during training time. To ensure reliable performance, the RL agents need to …