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03/17/2021
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm
In this paper, we investigate the sample complexity of policy evaluation...
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03/31/2020
Leverage the Average: an Analysis of Regularization in RL
Building upon the formalism of regularized Markov decision processes, we...
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10/21/2019
Momentum in Reinforcement Learning
We adapt the optimization's concept of momentum to reinforcement learnin...
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01/31/2019
A Theory of Regularized Markov Decision Processes
Many recent successful (deep) reinforcement learning algorithms make use...
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09/25/2018
Anderson Acceleration for Reinforcement Learning
Anderson acceleration is an old and simple method for accelerating the c...
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09/06/2018
How to Combine Tree-Search Methods in Reinforcement Learning
Finite-horizon lookahead policies are abundantly used in Reinforcement L...
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05/21/2018
Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning
Multiple-step lookahead policies have demonstrated high empirical compet...
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02/10/2018
Beyond the One Step Greedy Approach in Reinforcement Learning
The famous Policy Iteration algorithm alternates between policy improvem...
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05/13/2014
Rate of Convergence and Error Bounds for LSTD(λ)
We consider LSTD(λ), the least-squares temporal-difference algorithm wit...
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05/12/2014
Approximate Policy Iteration Schemes: A Comparison
We consider the infinite-horizon discounted optimal control problem form...
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06/06/2013
Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee
Local Policy Search is a popular reinforcement learning approach for han...
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06/03/2013
On the Performance Bounds of some Policy Search Dynamic Programming Algorithms
We consider the infinite-horizon discounted optimal control problem form...
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06/03/2013
Improved and Generalized Upper Bounds on the Complexity of Policy Iteration
Given a Markov Decision Process (MDP) with n states and a totalnumber m ...
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04/20/2013
Tight Performance Bounds for Approximate Modified Policy Iteration with Non-Stationary Policies
We consider approximate dynamic programming for the infinite-horizon sta...
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04/15/2013
Off-policy Learning with Eligibility Traces: A Survey
In the framework of Markov Decision Processes, off-policy learning, that...
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11/29/2012
On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes
We consider infinite-horizon stationary γ-discounted Markov Decision Pro...
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06/27/2012
A Dantzig Selector Approach to Temporal Difference Learning
LSTD is a popular algorithm for value function approximation. Whenever t...
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05/14/2012
Approximate Modified Policy Iteration
Modified policy iteration (MPI) is a dynamic programming (DP) algorithm ...
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03/25/2012
On the Use of Non-Stationary Policies for Infinite-Horizon Discounted Markov Decision Processes
We consider infinite-horizon γ-discounted Markov Decision Processes, for...
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11/19/2010
