Bruno Scherrer
19 papers · 2008–2020 · 5 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (11) π Interdisciplinary Bridge π Conference Polyglot (5)
π
Academic Marathon
(12)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
πΊ
Lone Wolf
(3)
π§¬
Topic Evolution
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(73)
π
Trend Setter
π
Century Club
(19)
π₯
Unstoppable
(5)
π
Conference Pioneer
Conferences
ICML (7)
NIPS (6)
JMLR (3)
AISTATS (2)
AAAI (1)
Top co-authors
Keywords
reinforcement learning
(9)
policy iteration
(9)
markov decision process
(8)
value iteration
(8)
approximate dynamic programming
(5)
discount factor
(4)
value function approximation
(4)
markov game
(3)
non-stationary policy
(3)
value function
(2)
optimal control
(2)
policy optimization
(2)
eligibility trace
(2)
game ai
(2)
markov decision processes
(2)
zero-sum game
(2)
policy learning
(2)
policy improvement
(2)
approximate policy iteration
(2)
iteration complexity
(2)
Papers
Momentum in Reinforcement Learning
AISTATS 2020
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
NIPS 2020
A Theory of Regularized Markov Decision Processes
ICML 2019
How to Combine Tree-Search Methods in Reinforcement Learning
AAAI 2019
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
NIPS 2018
Beyond the One-Step Greedy Approach in Reinforcement Learning
ICML 2018
Softened Approximate Policy Iteration for Markov Games
ICML 2016
On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games
AISTATS 2016
On the Rate of Convergence and Error Bounds for LSTD(Ξ»)
ICML 2015
Approximate Modified Policy Iteration and its Application to the Game of Tetris
JMLR 2015
Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games
ICML 2015
Non-Stationary Approximate Modified Policy Iteration
ICML 2015
Approximate Policy Iteration Schemes: A Comparison
ICML 2014
Off-policy Learning With Eligibility Traces: A Survey
JMLR 2014
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris
NIPS 2013
Improved and Generalized Upper Bounds on the Complexity of Policy Iteration
NIPS 2013
Performance Bounds for Ξ» Policy Iteration and Application to the Game of Tetris
JMLR 2013
On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes
NIPS 2012
Biasing Approximate Dynamic Programming with a Lower Discount Factor
NIPS 2008