David Abel | DeepAI

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∙ 12/20/2022

Settling the Reward Hypothesis

The reward hypothesis posits that, "all of what we mean by goals and pur...

4 Michael Bowling, et al. ∙

∙ 03/01/2022

A Theory of Abstraction in Reinforcement Learning

Reinforcement learning defines the problem facing agents that learn to m...

0 David Abel, et al. ∙

∙ 11/01/2021

On the Expressivity of Markov Reward

Reward is the driving force for reinforcement-learning agents. This pape...

15 David Abel, et al. ∙

∙ 10/07/2021

Bad-Policy Density: A Measure of Reinforcement Learning Hardness

Reinforcement learning is hard in general. Yet, in many specific environ...

7 David Abel, et al. ∙

∙ 05/14/2021

Control of mental representations in human planning

One of the most striking features of human cognition is the capacity to ...

5 Mark K Ho, et al. ∙

∙ 02/27/2021

Revisiting Peng's Q(λ) for Modern Reinforcement Learning

Off-policy multi-step reinforcement learning algorithms consist of conse...

0 Tadashi Kozuno, et al. ∙

∙ 06/26/2020

What can I do here? A Theory of Affordances in Reinforcement Learning

Reinforcement learning algorithms usually assume that all actions are al...

0 Khimya Khetarpal, et al. ∙

∙ 02/13/2020

The Efficiency of Human Cognition Reflects Planned Information Processing

Planning is useful. It lets people take actions that have desirable long...

7 Mark K Ho, et al. ∙

∙ 02/08/2020

Learning State Abstractions for Transfer in Continuous Control

Can simple algorithms with a good representation solve challenging reinf...

11 Kavosh Asadi, et al. ∙

∙ 01/15/2020

Lipschitz Lifelong Reinforcement Learning

We consider the problem of knowledge transfer when an agent is facing a ...

18 Erwan Lecarpentier, et al. ∙

∙ 03/02/2019

Discovering Options for Exploration by Minimizing Cover Time

One of the main challenges in reinforcement learning is solving tasks wi...

0 Yuu Jinnai, et al. ∙

∙ 12/03/2018

Mitigating Planner Overfitting in Model-Based Reinforcement Learning

An agent with an inaccurate model of its environment faces a difficult c...

0 Dilip Arumugam, et al. ∙

∙ 10/16/2018

Finding Options that Minimize Planning Time

While adding temporally abstract actions, or options, to an agent's acti...

4 Yuu Jinnai, et al. ∙

∙ 06/02/2017

Latent Attention Networks

Deep neural networks are able to solve tasks across a variety of domains...

0 Christopher Grimm, et al. ∙

∙ 01/15/2017

Near Optimal Behavior via Approximate State Abstraction

The combinatorial explosion that plagues planning and reinforcement lear...

0 David Abel, et al. ∙

∙ 01/15/2017

Agent-Agnostic Human-in-the-Loop Reinforcement Learning

Providing Reinforcement Learning agents with expert advice can dramatica...

0 David Abel, et al. ∙

∙ 03/14/2016

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains

High-dimensional observations and complex real-world dynamics present ma...

0 David Abel, et al. ∙

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