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20202025
most citedHybrid RL: Using Both Offline and Online Data Can Make RL Efficient

7 citations · 22 across the 13 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.LG2022

Representation Learning for General-sum Low-rank Markov Games

Chengzhuo Ni, Yuda Song, Xuezhou Zhang +2

We study multi-agent general-sum Markov games with nonlinear function approximation. We focus on low-rank Markov games whose transition matrix admits a hidden low-rank structure on…

cs.LG2022★ 7 cited

Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Yuda Song, Yifei Zhou, Ayush Sekhari +3

We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online inte…

cs.LG2022★ 2 cited

Provable Benefits of Representational Transfer in Reinforcement Learning

Alekh Agarwal, Yuda Song, Wen Sun +3

We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used…

cs.LG2022

Online No-regret Model-Based Meta RL for Personalized Navigation

Yuda Song, Ye Yuan, Wen Sun +1

The interaction between a vehicle navigation system and the driver of the vehicle can be formulated as a model-based reinforcement learning problem, where the navigation systems (a…

cs.LG2022

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach

Xuezhou Zhang, Yuda Song, Masatoshi Uehara +3

We present BRIEE (Block-structured Representation learning with Interleaved Explore Exploit), an algorithm for efficient reinforcement learning in Markov Decision Processes with bl…