257 citations · 1k across the 96 of their papers we have counts for
17 papers · 1 filter
An Investigation of Time Reversal Symmetry in Reinforcement Learning
Brett Barkley, Amy Zhang, David Fridovich-Keil
One of the fundamental challenges associated with reinforcement learning (RL) is that collecting sufficient data can be both time-consuming and expensive. In this paper, we formali…
Accelerating Exploration with Unlabeled Prior Data
Qiyang Li, Jason Zhang, Dibya Ghosh +2
Learning to solve tasks from a sparse reward signal is a major challenge for standard reinforcement learning (RL) algorithms. However, in the real world, agents rarely need to solv…
SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning
Harshit Sikchi, Rohan Chitnis, Ahmed Touati +3
Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with learning to achieve multiple goals in an environment purely from offline datasets using sparse reward function…
-Policy Gradients: A General Framework for Goal Conditioned RL using -Divergences
Siddhant Agarwal, Ishan Durugkar, Peter Stone +1
Goal-Conditioned Reinforcement Learning (RL) problems often have access to sparse rewards where the agent receives a reward signal only when it has achieved the goal, making policy…
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
Rui Yang, Han Zhong, Jiawei Xu +4
Offline reinforcement learning (RL) presents a promising approach for learning reinforced policies from offline datasets without the need for costly or unsafe interactions with the…
Motif: Intrinsic Motivation from Artificial Intelligence Feedback
Martin Klissarov, Pierluca D'Oro, Shagun Sodhani +5
Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging. In this paper, we propose Motif, a general method to interface such prior…
Generalization Across Observation Shifts in Reinforcement Learning
Anuj Mahajan, Amy Zhang
Learning policies which are robust to changes in the environment are critical for real world deployment of Reinforcement Learning agents. They are also necessary for achieving good…
Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistance
Jie Zhu, Mengsha Hu, Xueyao Liang +3
Robots assist humans in various activities, from daily living public service (e.g., airports and restaurants), and to collaborative manufacturing. However, it is risky to assume th…
Structure in Deep Reinforcement Learning: A Survey and Open Problems
Aditya Mohan, Amy Zhang, Marius Lindauer
Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous a…
LIV: Language-Image Representations and Rewards for Robotic Control
Yecheng Jason Ma, William Liang, Vaidehi Som +4
We present Language-Image Value learning (LIV), a unified objective for vision-language representation and reward learning from action-free videos with text annotations. Exploiting…
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem
Paul Barde, Jakob Foerster, Derek Nowrouzezahrai +1
Training multiple agents to coordinate is an essential problem with applications in robotics, game theory, economics, and social sciences. However, most existing Multi-Agent Reinfo…
When should we prefer Decision Transformers for Offline Reinforcement Learning?
Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard +2
Offline reinforcement learning (RL) allows agents to learn effective, return-maximizing policies from a static dataset. Three popular algorithms for offline RL are Conservative Q-L…
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
Tongzhou Wang, Antonio Torralba, Phillip Isola +1
In goal-reaching reinforcement learning (RL), the optimal value function has a particular geometry, called quasimetric structure. This paper introduces Quasimetric Reinforcement Le…
BC-IRL: Learning Generalizable Reward Functions from Demonstrations
Andrew Szot, Amy Zhang, Dhruv Batra +2
How well do reward functions learned with inverse reinforcement learning (IRL) generalize? We illustrate that state-of-the-art IRL algorithms, which maximize a maximum-entropy obje…
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement
Michael Chang, Alyssa L. Dayan, Franziska Meier +3
Object rearrangement is a challenge for embodied agents because solving these tasks requires generalizing across a combinatorially large set of configurations of entities and their…
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
Harshit Sikchi, Qinqing Zheng, Amy Zhang +1
The goal of reinforcement learning (RL) is to find a policy that maximizes the expected cumulative return. It has been shown that this objective can be represented as an optimizati…
Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
Hanlin Zhu, Amy Zhang
Goal-conditioned reinforcement learning (GCRL) refers to learning general-purpose skills that aim to reach diverse goals. In particular, offline GCRL only requires purely pre-colle…