52 citations · 222 across the 17 of their papers we have counts for
17 papers
On the Convergence of Monte Carlo UCB for Random-Length Episodic MDPs
Zixuan Dong, Che Wang, Keith Ross
In reinforcement learning, Monte Carlo algorithms update the Q function by averaging the episodic returns. In the Monte Carlo UCB (MC-UCB) algorithm, the action taken in each state…
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning
Che Wang, Xufang Luo, Keith Ross +1
We propose VRL3, a powerful data-driven framework with a simple design for solving challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major obstacle…
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
Yanqiu Wu, Xinyue Chen, Che Wang +2
Recent advances in model-free deep reinforcement learning (DRL) show that simple model-free methods can be highly effective in challenging high-dimensional continuous control tasks…
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
Yiming Zhang, Keith W. Ross
We develop theory and algorithms for average-reward on-policy Reinforcement Learning (RL). We first consider bounding the difference of the long-term average reward for two policie…
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
Xinyue Chen, Che Wang, Zijian Zhou +1
Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchma…
First Order Constrained Optimization in Policy Space
Yiming Zhang, Quan Vuong, Keith W. Ross
In reinforcement learning, an agent attempts to learn high-performing behaviors through interacting with the environment, such behaviors are often quantified in the form of a rewar…