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03/13/2023
Loss of Plasticity in Continual Deep Reinforcement Learning
The ability to learn continually is essential in a complex and changing ...
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01/26/2023
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
Off-policy learning from multistep returns is crucial for sample-efficie...
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01/26/2023
Deep Laplacian-based Options for Temporally-Extended Exploration
Selecting exploratory actions that generate a rich stream of experience ...
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11/15/2022
Agent-State Construction with Auxiliary Inputs
In many, if not every realistic sequential decision-making task, the dec...
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03/30/2022
Investigating the Properties of Neural Network Representations in Reinforcement Learning
In this paper we investigate the properties of representations learned b...
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03/21/2022
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
In reinforcement learning, the graph Laplacian has proved to be a valuab...
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02/07/2022
Reward-Respecting Subtasks for Model-Based Reinforcement Learning
To achieve the ambitious goals of artificial intelligence, reinforcement...
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09/22/2021
On Bonus-Based Exploration Methods in the Arcade Learning Environment
Research on exploration in reinforcement learning, as applied to Atari 2...
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08/12/2021
A functional mirror ascent view of policy gradient methods with function approximation
We use functional mirror ascent to propose a general framework (referred...
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01/13/2021
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Reinforcement learning methods trained on few environments rarely learn ...
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08/06/2019
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment
This paper provides an empirical evaluation of recently developed explor...
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09/29/2018
Generalization and Regularization in DQN
Deep reinforcement learning (RL) algorithms have shown an impressive abi...
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07/31/2018
Count-Based Exploration with the Successor Representation
The problem of exploration in reinforcement learning is well-understood ...
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03/23/2018
Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation
Here we propose using the successor representation (SR) to accelerate le...
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12/11/2017
The Eigenoption-Critic Framework
Eigenoptions (EOs) have been recently introduced as a promising idea for...
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10/30/2017
Eigenoption Discovery through the Deep Successor Representation
Options in reinforcement learning allow agents to hierarchically decompo...
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06/17/2016
Introspective Agents: Confidence Measures for General Value Functions
Agents of general intelligence deployed in real-world scenarios must ada...
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12/13/2015
True Online Temporal-Difference Learning
The temporal-difference methods TD(λ) and Sarsa(λ) form a core part of m...
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10/16/2014
Domain-Independent Optimistic Initialization for Reinforcement Learning
In Reinforcement Learning (RL), it is common to use optimistic initializ...
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12/13/2013