27 citations · 31 across the 5 of their papers we have counts for
11 papers
Characterizing the Action-Generalization Gap in Deep Q-Learning
Zhiyuan Zhou, Cameron Allen, Kavosh Asadi +1
We study the action generalization ability of deep Q-learning in discrete action spaces. Generalization is crucial for efficient reinforcement learning (RL) because it allows agent…
Coarse-Grained Smoothness for RL in Metric Spaces
Omer Gottesman, Kavosh Asadi, Cameron Allen +3
Principled decision-making in continuous state--action spaces is impossible without some assumptions. A common approach is to assume Lipschitz continuity of the Q-function. We show…
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback
Ishaan Shah, David Halpern, Kavosh Asadi +1
Fluid human-agent communication is essential for the future of human-in-the-loop reinforcement learning. An agent must respond appropriately to feedback from its human trainer even…
Learning State Abstractions for Transfer in Continuous Control
Kavosh Asadi, David Abel, Michael L. Littman
Can simple algorithms with a good representation solve challenging reinforcement learning problems? In this work, we answer this question in the affirmative, where we take "simple…
Deep Radial-Basis Value Functions for Continuous Control
Kavosh Asadi, Neev Parikh, Ronald E. Parr +2
A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned…
Lipschitz Lifelong Reinforcement Learning
Erwan Lecarpentier, David Abel, Kavosh Asadi +3
We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes (…