Timezone: »
This paper is about the study of B-FQI, an Approximated Value Iteration (AVI) algorithm that exploits a boosting procedure to estimate the action-value function in reinforcement learning problems. B-FQI is an iterative off-line algorithm that, given a dataset of transitions, builds an approximation of the optimal action-value function by summing the approximations of the Bellman residuals across all iterations. The advantage of such approach w.r.t. to other AVI methods is twofold: (1) while keeping the same function space at each iteration, B-FQI can represent more complex functions by considering an additive model; (2) since the Bellman residual decreases as the optimal value function is approached, regression problems become easier as iterations proceed. We study B-FQI both theoretically, providing also a finite-sample error upper bound for it, and empirically, by comparing its performance to the one of FQI in different domains and using different regression techniques.
Summary/NotesAuthor Information
Samuele Tosatto (Politecnico di Milano)
Samuele Tosatto joined the Institute for Intelligent Autonomous Systems (IAS) at TU Darmstadt in May 2017 as a Ph.D. student. Samuele received his bachelor degree as well as his master degree in software engineering from Polytechnic University of Milan. During his studies he focused on machine learning and more in particular in reinforcement learning. His thesis, entitled “Boosted Fitted Q-Iteration", was written under the supervision of Prof. Marcello Restelli PhD Matteo Pirotta and Ing Carlo D'Eramo.
Matteo Pirotta (SequeL - Inria Lille - Nord Europe)
Carlo D'Eramo (Politecnico di Milano)
Marcello Restelli (Politecnico di Milano)
Related Events (a corresponding poster, oral, or spotlight)
-
2017 Poster: Boosted Fitted Q-Iteration »
Mon Aug 7th 08:30 AM -- 12:00 PM Room Gallery
More from the Same Authors
-
2020 Poster: Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning »
Alberto Maria Metelli · Flavio Mazzolini · Lorenzo Bisi · Luca Sabbioni · Marcello Restelli -
2020 Poster: Sequential Transfer in Reinforcement Learning with a Generative Model »
Andrea Tirinzoni · Riccardo Poiani · Marcello Restelli -
2019 Poster: Reinforcement Learning in Configurable Continuous Environments »
Alberto Maria Metelli · Emanuele Ghelfi · Marcello Restelli -
2019 Oral: Reinforcement Learning in Configurable Continuous Environments »
Alberto Maria Metelli · Emanuele Ghelfi · Marcello Restelli -
2019 Poster: Transfer of Samples in Policy Search via Multiple Importance Sampling »
Andrea Tirinzoni · Mattia Salvini · Marcello Restelli -
2019 Oral: Transfer of Samples in Policy Search via Multiple Importance Sampling »
Andrea Tirinzoni · Mattia Salvini · Marcello Restelli -
2019 Poster: Optimistic Policy Optimization via Multiple Importance Sampling »
Matteo Papini · Alberto Maria Metelli · Lorenzo Lupo · Marcello Restelli -
2019 Oral: Optimistic Policy Optimization via Multiple Importance Sampling »
Matteo Papini · Alberto Maria Metelli · Lorenzo Lupo · Marcello Restelli -
2018 Poster: Importance Weighted Transfer of Samples in Reinforcement Learning »
Andrea Tirinzoni · Andrea Sessa · Matteo Pirotta · Marcello Restelli -
2018 Poster: Stochastic Variance-Reduced Policy Gradient »
Matteo Papini · Damiano Binaghi · Giuseppe Canonaco · Matteo Pirotta · Marcello Restelli -
2018 Poster: Configurable Markov Decision Processes »
Alberto Maria Metelli · Mirco Mutti · Marcello Restelli -
2018 Oral: Importance Weighted Transfer of Samples in Reinforcement Learning »
Andrea Tirinzoni · Andrea Sessa · Matteo Pirotta · Marcello Restelli -
2018 Oral: Configurable Markov Decision Processes »
Alberto Maria Metelli · Mirco Mutti · Marcello Restelli -
2018 Oral: Stochastic Variance-Reduced Policy Gradient »
Matteo Papini · Damiano Binaghi · Giuseppe Canonaco · Matteo Pirotta · Marcello Restelli -
2018 Poster: Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning »
Ronan Fruit · Matteo Pirotta · Alessandro Lazaric · Ronald Ortner -
2018 Oral: Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning »
Ronan Fruit · Matteo Pirotta · Alessandro Lazaric · Ronald Ortner