153 citations · 171 across the 12 of their papers we have counts for
15 papers
Enhancing Robotic Perception and Adaptability through Sensor Fusion and Origami-Inspired Designs
Namai Chandra, Jaison Jose, Kavi Arya +1
Compact mobile robots must recover scene geometry under changing lighting and surface texture while working within tight payload and cost limits. We present a compact mobile robot…
Decentralized Multi-Robot Exploration with Probabilistic Peer Intent and Multi-hop Plan Propagation
Saurbh Singh Jamwal, Nived Chebrolu, Shivaram Kalyanakrishnan
Efficient coordination under limited communication remains a key challenge in decentralized multi-robot exploration. While centralized approaches benefit from global information sh…
On-line Learning in Tree MDPs by Treating Policies as Bandit Arms
Anvay Shah, Ramsundar Anandanarayanan, Sharayu Moharir +1
A Tree Markov Decision Problem (T-MDP) is a finite-horizon MDP with a starting state , in which every state is reachable from through exactly one state-action trajec…
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…
Efficient Computation of Blackwell Optimal Policies using Rational Functions
Dibyangshu Mukherjee, Shivaram Kalyanakrishnan
Markov Decision Problems (MDPs) provide a foundational framework for modelling sequential decision-making across diverse domains, guided by optimality criteria such as discounted a…
Howard's Policy Iteration is Subexponential for Deterministic Markov Decision Problems with Rewards of Fixed Bit-size and Arbitrary Discount Factor
Dibyangshu Mukherjee, Shivaram Kalyanakrishnan
Howard's Policy Iteration (HPI) is a classic algorithm for solving Markov Decision Problems (MDPs). HPI uses a "greedy" switching rule to update from any non-optimal policy to a do…
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…
Upper Bounds for All and Max-gain Policy Iteration Algorithms on Deterministic MDPs
Ritesh Goenka, Eashan Gupta, Sushil Khyalia +3
Policy Iteration (PI) is a widely used family of algorithms to compute optimal policies for Markov Decision Problems (MDPs). We derive upper bounds on the running time of PI on Det…
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence
Peter Stone, Rodney Brooks, Erik Brynjolfsson +14
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial…
PAC Mode Estimation using PPR Martingale Confidence Sequences
Shubham Anand Jain, Rohan Shah, Sanit Gupta +7
We consider the problem of correctly identifying the \textit{mode} of a discrete distribution with sufficiently high probability by observing a sequence of i.i.d. sam…
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…
RLWS: A Reinforcement Learning based GPU Warp Scheduler
Jayvant Anantpur, Nagendra Gulur Dwarakanath, Shivaram Kalyanakrishnan +2
The Streaming Multiprocessors (SMs) of a Graphics Processing Unit (GPU) execute instructions from a group of consecutive threads, called warps. At each cycle, an SM schedules a war…