6 papers · 1 filter
ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization
Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano +1
Reward shaping is critical in reinforcement learning (RL), particularly for complex tasks where sparse rewards can hinder learning. However, choosing effective shaping rewards from…
State-free Reinforcement Learning
Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang
In this work, we study the \textit{state-free RL} problem, where the algorithm does not have the states information before interacting with the environment. Specifically, denote th…
Second Order Bounds for Contextual Bandits with Function Approximation
Aldo Pacchiano
Many works have developed no-regret algorithms for contextual bandits with function approximation, where the mean reward function over context-action pairs belongs to a function cl…
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives
Aida Afshar, Aldo Pacchiano
The performance of reinforcement learning (RL) algorithms is sensitive to the choice of hyperparameters, with the learning rate being particularly influential. RL algorithms fail t…
Provable Interactive Learning with Hindsight Instruction Feedback
Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
We study interactive learning in a setting where the agent has to generate a response (e.g., an action or trajectory) given a context and an instruction. In contrast, to typical ap…
Multiple-policy Evaluation via Density Estimation
Yilei Chen, Aldo Pacchiano, Ioannis Ch. Paschalidis
We study the multiple-policy evaluation problem where we are given a set of policies and the goal is to evaluate their performance (expected total reward over a fixed horizon)…