Yi Ouyang
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar, Rahul Jain
Neural Information Processing Systems http://nips.cc/
A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization
Cedric Josz, Yi Ouyang, Richard Zhang, Javad Lavaei, Somayeh Sojoudi
Neural Information Processing Systems http://nips.cc/
A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization
Cedric Josz, Yi Ouyang, Richard Zhang, Javad Lavaei, Somayeh Sojoudi
We study the set of continuous functions that admit no spurious local optima (i.e.
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar, Rahul Jain
We consider the problem of learning an unknown Markov Decision Process (MDP) that is weakly communicating in the infinite horizon setting. We propose a Thompson Sampling-based reinforcement learning algorithm with dynamic episodes (TSDE). At the beginning of each episode, the algorithm generates a sample from the posterior distribution over the unknown model parameters. It then follows the optimal stationary policy for the sampled model for the rest of the episode. The duration of each episode is dynamically determined by two stopping criteria.