49 citations · 235 across the 48 of their papers we have counts for
10 papers · 1 filter
A Simple Decentralized Cross-Entropy Method
Zichen Zhang, Jun Jin, Martin Jagersand +2
Cross-Entropy Method (CEM) is commonly used for planning in model-based reinforcement learning (MBRL) where a centralized approach is typically utilized to update the sampling dist…
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
Zichen Zhang, Johannes Kirschner, Junxi Zhang +4
A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle. Yet, many applications involve c…
Latent Variable Representation for Reinforcement Learning
Tongzheng Ren, Chenjun Xiao, Tianjun Zhang +5
Deep latent variable models have achieved significant empirical successes in model-based reinforcement learning (RL) due to their expressiveness in modeling complex transition dyna…
TEMPERA: Test-Time Prompting via Reinforcement Learning
Tianjun Zhang, Xuezhi Wang, Denny Zhou +2
Careful prompt design is critical to the use of large language models in zero-shot or few-shot learning. As a consequence, there is a growing interest in automated methods to desig…
Learning to Optimize with Stochastic Dominance Constraints
Hanjun Dai, Yuan Xue, Niao He +4
In real-world decision-making, uncertainty is important yet difficult to handle. Stochastic dominance provides a theoretically sound approach for comparing uncertain quantities, bu…
Dichotomy of Control: Separating What You Can Control from What You Cannot
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel +1
Future- or return-conditioned supervised learning is an emerging paradigm for offline reinforcement learning (RL), where the future outcome (i.e., return) associated with an observ…
A Parametric Class of Approximate Gradient Updates for Policy Optimization
Ramki Gummadi, Saurabh Kumar, Junfeng Wen +1
Approaches to policy optimization have been motivated from diverse principles, based on how the parametric model is interpreted (e.g. value versus policy representation) or how the…
Discrete Langevin Sampler via Wasserstein Gradient Flow
Haoran Sun, Hanjun Dai, Bo Dai +2
It is known that gradient-based MCMC samplers for continuous spaces, such as Langevin Monte Carlo (LMC), can be derived as particle versions of a gradient flow that minimizes KL di…
Chain of Thought Imitation with Procedure Cloning
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel +1
Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning proble…
Reinforcement Teaching
Calarina Muslimani, Alex Lewandowski, Dale Schuurmans +2
Machine learning algorithms learn to solve a task, but are unable to improve their ability to learn. Meta-learning methods learn about machine learning algorithms and improve them…