Pratik Gajane

Autonomous exploration for navigating in non-stationary CMPs

2019 · arXiv (Cornell University)

We consider a setting in which the objective is to learn to navigate in a controlled Markov process (CMP) where transition probabilities may abruptly change. For this setting, we propose a performance measure called exploration …

Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need

2023 · arXiv (Cornell University)

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 achieves near-optimal regret bounds while being advantageous empirically compared to the …

Adversarial Multi-dueling Bandits

2024 · arXiv (Cornell University)

We introduce the problem of regret minimization in adversarial multi-dueling bandits. While adversarial preferences have been studied in dueling bandits, they have not been explored in multi-dueling bandits. In this setting, the learner is required …