160 citations · 274 across the 23 of their papers we have counts for
4 papers · 1 filter
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achi…
Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards
Ronald C. van den Broek, Rik Litjens, Tobias Sagis +3
Decision-making problems of sequential nature, where decisions made in the past may have an impact on the future, are used to model many practically important applications. In some…
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning
Jiong Li, Pratik Gajane
Sparsity of rewards while applying a deep reinforcement learning method negatively affects its sample-efficiency. A viable solution to deal with the sparsity of rewards is to learn…
Local Differential Privacy for Sequential Decision Making in a Changing Environment
Pratik Gajane
We study the problem of preserving privacy while still providing high utility in sequential decision making scenarios in a changing environment. We consider abruptly changing envir…