22 citations · 33 across the 10 of their papers we have counts for
4 papers · 1 filter
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning
Yash Satsangi, Paniz Behboudian
A key challenge for a reinforcement learning (RL) agent is to incorporate external/expert1 advice in its learning. The desired goals of an algorithm that can shape the learning of…
Learning to Be Cautious
Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai +2
A key challenge in the field of reinforcement learning is to develop agents that behave cautiously in novel situations. It is generally impossible to anticipate all situations that…
Exploiting Submodular Value Functions For Scaling Up Active Perception
Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek +1
In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty about one or more hidden variables. While partially observable Markov decision proce…
Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Yash Satsangi, Sungsu Lim, Shimon Whiteson +2
Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the agent's uncertainty. For exa…