3 citations · 7 across the 4 of their papers we have counts for
4 papers
Convex Q Learning in a Stochastic Environment: Extended Version
Fan Lu, Sean Meyn
The paper introduces the first formulation of convex Q-learning for Markov decision processes with function approximation. The algorithms and theory rest on a relaxation of a dual…
Sufficient Exploration for Convex Q-learning
Fan Lu, Prashant Mehta, Sean Meyn +1
In recent years there has been a collective research effort to find new formulations of reinforcement learning that are simultaneously more efficient and more amenable to analysis.…
Model-Free Characterizations of the Hamilton-Jacobi-Bellman Equation and Convex Q-Learning in Continuous Time
Fan Lu, Joel Mathias, Sean Meyn +1
Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a pr…
Adaptive Leader-Follower Formation Control and Obstacle Avoidance via Deep Reinforcement Learning
Yanlin Zhou, Fan Lu, George Pu +5
We propose a deep reinforcement learning (DRL) methodology for the tracking, obstacle avoidance, and formation control of nonholonomic robots. By separating vision-based control in…