4 papers
Policy Gradient Methods for Non-Markovian Reinforcement Learning
Avik Kar, Siddharth Chandak, Rahul Singh +4
We study policy gradient methods for reinforcement learning in non-Markovian decision processes (NMDPs), where observations and rewards depend on the entire interaction history. To…
High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
Avik Kar, Siddharth Chandak, Rahul Singh +3
We present the first uniform-in-time high-probability bound for SGD under the PL condition, where the gradient noise contains both Markovian and martingale difference components. T…
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning
Avik Kar, Rahul Singh
We study the infinite-horizon average-reward reinforcement learning (RL) for continuous space Lipschitz MDPs in which an agent can play policies from a given set . The proposed…
Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces
Avik Kar, Rahul Singh
We study infinite-horizon average-reward reinforcement learning (RL) for Lipschitz MDPs, a broad class that subsumes several important classes such as linear and RKHS MDPs, functio…