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06/30/2023
Resetting the Optimizer in Deep RL: An Empirical Study
We focus on the task of approximating the optimal value function in deep...
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06/30/2023
TD Convergence: An Optimization Perspective
We study the convergence behavior of the celebrated temporal-difference ...
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05/11/2022
Characterizing the Action-Generalization Gap in Deep Q-Learning
We study the action generalization ability of deep Q-learning in discret...
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12/10/2021
Deep Q-Network with Proximal Iteration
We employ Proximal Iteration for value-function optimization in reinforc...
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10/23/2021
Coarse-Grained Smoothness for RL in Metric Spaces
Principled decision-making in continuous state–action spaces is impossib...
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09/15/2021
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback
Fluid human-agent communication is essential for the future of human-in-...
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02/08/2020
Learning State Abstractions for Transfer in Continuous Control
Can simple algorithms with a good representation solve challenging reinf...
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02/05/2020
Deep RBF Value Functions for Continuous Control
A core operation in reinforcement learning (RL) is finding an action tha...
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01/15/2020
Lipschitz Lifelong Reinforcement Learning
We consider the problem of knowledge transfer when an agent is facing a ...
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05/30/2019
Combating the Compounding-Error Problem with a Multi-step Model
Model-based reinforcement learning is an appealing framework for creatin...
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12/03/2018
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
An agent with an inaccurate model of its environment faces a difficult c...
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10/31/2018
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
When environmental interaction is expensive, model-based reinforcement l...
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06/01/2018
Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning
Learning a generative model is a key component of model-based reinforcem...
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04/19/2018
Lipschitz Continuity in Model-based Reinforcement Learning
Model-based reinforcement-learning methods learn transition and reward m...
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09/01/2017
Mean Actor Critic
We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action...
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02/10/2017
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
End-to-end learning of recurrent neural networks (RNNs) is an attractive...
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12/18/2016
Sample-efficient Deep Reinforcement Learning for Dialog Control
Representing a dialog policy as a recurrent neural network (RNN) is attr...
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12/16/2016
