4 papers · 1 filter
Replicable Dueling Bandits
Rohan Deb, Aadirupa Saha, Udaya Ghai +2
Dueling bandits choose each comparison based on earlier noisy outcomes, so two runs on the same preferences can compare different pairs. We study replicable dueling bandits, where…
Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
Eun Go, Rohan Deb, Arindam Banerjee
Reinforcement learning agents for portfolio management are typically trained and deployed as static policies, with no mechanism for policy adjustments using price forecasts at infe…
Replicable Bandits with UCB based Exploration
Rohan Deb, Udaya Ghai, Karan Singh +1
We study replicable algorithms for stochastic multi-armed bandits (MAB) and linear bandits with UCB (Upper Confidence Bound) based exploration. A bandit algorithm is -replicable…
Inference Time Policy Optimization for Offline RL with Differentiable World Models
Rohan Deb, Stephen J. Wright, Arindam Banerjee
Offline Reinforcement Learning (RL) learns optimal policies from fixed datasets, training a policy once and deploying it at inference time without further refinement. Inspired by m…