153 citations · 171 across the 12 of their papers we have counts for
6 papers · 1 filter
Using Common Random Numbers for Simulation-based Planning with Rollouts
Sandarbh Yadav, Frederic J Maliakkal, Harshad Khadilkar +1
Simulation-based planning with rollouts is a widely-deployed technique for decision making in stochastic environments. The primary instrument of simulation-based planning is a samp…
A View of the Certainty-Equivalence Method for PAC RL as an Application of the Trajectory Tree Method
Shivaram Kalyanakrishnan, Sheel Shah, Santhosh Kumar Guguloth
Reinforcement learning (RL) enables an agent interacting with an unknown MDP to optimise its behaviour by observing transitions sampled from . A natural entity that emerges…
An Analysis of Frame-skipping in Reinforcement Learning
Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat +5
In the practice of sequential decision making, agents are often designed to sense state at regular intervals of time steps, , ignoring state information in between sensi…
Lower Bounds for Policy Iteration on Multi-action MDPs
Kumar Ashutosh, Sarthak Consul, Bhishma Dedhia +3
Policy Iteration (PI) is a classical family of algorithms to compute an optimal policy for any given Markov Decision Problem (MDP). The basic idea in PI is to begin with some initi…
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory
Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan
In this paper, we propose a constant word (RAM model) algorithm for regret minimisation for both finite and infinite Stochastic Multi-Armed Bandit (MAB) instances. Most of the exis…
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan
We consider the problem of identifying any out of the best arms in an -armed stochastic multi-armed bandit. Framed in the PAC setting, this particular problem generalise…