560 citations · 3.3k across the 60 of their papers we have counts for
6 papers · 2 filters
Measuring and Characterizing Generalization in Deep Reinforcement Learning
Sam Witty, Jun Ki Lee, Emma Tosch +3
Deep reinforcement-learning methods have achieved remarkable performance on challenging control tasks. Observations of the resulting behavior give the impression that the agent has…
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
Dilip Arumugam, David Abel, Kavosh Asadi +5
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is opti…
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
Kavosh Asadi, Evan Cater, Dipendra Misra +1
When environmental interaction is expensive, model-based reinforcement learning offers a solution by planning ahead and avoiding costly mistakes. Model-based agents typically learn…
Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning
Kavosh Asadi, Evan Cater, Dipendra Misra +1
Learning a generative model is a key component of model-based reinforcement learning. Though learning a good model in the tabular setting is a simple task, learning a useful model…
Transfer with Model Features in Reinforcement Learning
Lucas Lehnert, Michael L. Littman
A key question in Reinforcement Learning is which representation an agent can learn to efficiently reuse knowledge between different tasks. Recently the Successor Representation wa…
Lipschitz Continuity in Model-based Reinforcement Learning
Kavosh Asadi, Dipendra Misra, Michael L. Littman
We examine the impact of learning Lipschitz continuous models in the context of model-based reinforcement learning. We provide a novel bound on multi-step prediction error of Lipsc…