81 citations · 106 across the 10 of their papers we have counts for
10 papers
Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
Hongming Zhang, Tongzheng Ren, Chenjun Xiao +2
In most real-world reinforcement learning applications, state information is only partially observable, which breaks the Markov decision process assumption and leads to inferior pe…
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning
Xiaohan Hu, Yi Ma, Chenjun Xiao +2
One of the fundamental challenges for offline reinforcement learning (RL) is ensuring robustness to data distribution. Whether the data originates from a near-optimal policy or not…
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
Xutong Zhao, Yangchen Pan, Chenjun Xiao +2
Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration method that effectively encourages cooperat…
The In-Sample Softmax for Offline Reinforcement Learning
Chenjun Xiao, Han Wang, Yangchen Pan +2
Reinforcement learning (RL) agents can leverage batches of previously collected data to extract a reasonable control policy. An emerging issue in this offline RL setting, however,…
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…
Understanding the Effect of Stochasticity in Policy Optimization
Jincheng Mei, Bo Dai, Chenjun Xiao +2
We study the effect of stochasticity in on-policy policy optimization, and make the following four contributions. First, we show that the preferability of optimization methods depe…
The Curse of Passive Data Collection in Batch Reinforcement Learning
Chenjun Xiao, Ilbin Lee, Bo Dai +2
In high stake applications, active experimentation may be considered too risky and thus data are often collected passively. While in simple cases, such as in bandits, passive and a…
On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao, Yifan Wu, Tor Lattimore +5
Batch policy optimization considers leveraging existing data for policy construction before interacting with an environment. Although interest in this problem has grown significant…
On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei, Chenjun Xiao, Csaba Szepesvari +1
We make three contributions toward better understanding policy gradient methods in the tabular setting. First, we show that with the true gradient, policy gradient with a softmax p…
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Chenjun Xiao, Yifan Wu, Chen Ma +2
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate. An algorit…