25 citations · 26 across the 7 of their papers we have counts for
7 papers
Diffusing States and Matching Scores: A New Framework for Imitation Learning
Runzhe Wu, Yiding Chen, Gokul Swamy +2
Adversarial Imitation Learning is traditionally framed as a two-player zero-sum game between a learner and an adversarially chosen cost function, and can therefore be thought of as…
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics
Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy +1
We study computationally and statistically efficient Reinforcement Learning algorithms for the linear Bellman Complete setting. This setting uses linear function approximation to c…
Making RL with Preference-based Feedback Efficient via Randomization
Runzhe Wu, Wen Sun
Reinforcement Learning algorithms that learn from human feedback (RLHF) need to be efficient in terms of statistical complexity, computational complexity, and query complexity. In…
Selective Sampling and Imitation Learning via Online Regression
Ayush Sekhari, Karthik Sridharan, Wen Sun +1
We consider the problem of Imitation Learning (IL) by actively querying noisy expert for feedback. While imitation learning has been empirically successful, much of prior work assu…
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
Kaiwen Wang, Kevin Zhou, Runzhe Wu +2
While distributional reinforcement learning (DistRL) has been empirically effective, the question of when and why it is better than vanilla, non-distributional RL has remained unan…
Distributional Offline Policy Evaluation with Predictive Error Guarantees
Runzhe Wu, Masatoshi Uehara, Wen Sun
We study the problem of estimating the distribution of the return of a policy using an offline dataset that is not generated from the policy, i.e., distributional offline policy ev…
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Ming Zhou, Ziyu Wan, Hanjing Wang +6
Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms, which produces a self-generated se…