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Dimitrakakis, Christos

Nom
Dimitrakakis, Christos
Affiliation principale
Fonction
Professor
Email
christos.dimitrakakis@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/30936
_
0000-0002-5367-5189

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  • Publication
    Accès libre
    Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
    (2023)
    Hannes Eriksson
    ;
    Debabrota Basu
    ;
    Tommy Tram
    ;
    Mina Alibeigi
    ;
    Dimitrakakis, Christos 
    In this paper, we study the problem of transferring the available Markov Decision Process (MDP) models to learn and plan efficiently in an unknown but similar MDP. We refer to it as \textit{Model Transfer Reinforcement Learning (MTRL)} problem. First, we formulate MTRL for discrete MDPs and Linear Quadratic Regulators (LQRs) with continuous state actions. Then, we propose a generic two-stage algorithm, MLEMTRL, to address the MTRL problem in discrete and continuous settings. In the first stage, MLEMTRL uses a \textit{constrained Maximum Likelihood Estimation (MLE)}-based approach to estimate the target MDP model using a set of known MDP models. In the second stage, using the estimated target MDP model, MLEMTRL deploys a model-based planning algorithm appropriate for the MDP class. Theoretically, we prove worst-case regret bounds for MLEMTRL both in realisable and non-realisable settings. We empirically demonstrate that MLEMTRL allows faster learning in new MDPs than learning from scratch and achieves near-optimal performance depending on the similarity of the available MDPs and the target MDP.
  • Publication
    Accès libre
    Minimax-Bayes Reinforcement Learning
    (PMLR, 2023)
    Thomas Kleine Buening
    ;
    Dimitrakakis, Christos 
    ;
    Hannes Eriksson
    ;
    Divya Grover
    ;
    Emilio Jorge
    While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prior distribution. One idea is to employ a worst-case prior. However, this is not as easy to specify in sequential decision making as in simple statistical estimation problems. This paper studies (sometimes approximate) minimax-Bayes solutions for various reinforcement learning problems to gain insights into the properties of the corresponding priors and policies. We find that while the worst-case prior depends on the setting, the corresponding minimax policies are more robust than those that assume a standard (i.e. uniform) prior.
  • Publication
    Accès libre
    Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty
    (2022-03-18T16:40:30Z)
    Hannes Eriksson
    ;
    Debabrota Basu
    ;
    Mina Alibeigi
    ;
    Dimitrakakis, Christos 
    In stochastic games with incomplete information, the uncertainty is evoked by the lack of knowledge about a player's own and the other players' types, i.e. the utility function and the policy space, and also the inherent stochasticity of different players' interactions. In existing literature, the risk in stochastic games has been studied in terms of the inherent uncertainty evoked by the variability of transitions and actions. In this work, we instead focus on the risk associated with the \textit{uncertainty over types}. We contrast this with the multi-agent reinforcement learning framework where the other agents have fixed stationary policies and investigate risk-sensitiveness due to the uncertainty about the other agents' adaptive policies. We propose risk-sensitive versions of existing algorithms proposed for risk-neutral stochastic games, such as Iterated Best Response (IBR), Fictitious Play (FP) and a general multi-objective gradient approach using dual ascent (DAPG). Our experimental analysis shows that risk-sensitive DAPG performs better than competing algorithms for both social welfare and general-sum stochastic games.
  • Publication
    Accès libre
    Environment Design for Inverse Reinforcement Learning
    (2022)
    Thomas Kleine Buening
    ;
    Dimitrakakis, Christos 
    The task of learning a reward function from expert demonstrations suffers from high sample complexity as well as inherent limitations to what can be learned from demonstrations in a given environment. As the samples used for reward learning require human input, which is generally expensive, much effort has been dedicated towards designing more sample-efficient algorithms. Moreover, even with abundant data, current methods can still fail to learn insightful reward functions that are robust to minor changes in the environment dynamics. We approach these challenges differently than prior work by improving the sample-efficiency as well as the robustness of learned rewards through adaptively designing a sequence of demonstration environments for the expert to act in. We formalise a framework for this environment design process in which learner and expert repeatedly interact, and construct algorithms that actively seek information about the rewards by carefully curating environments for the human to demonstrate the task in.
  • Publication
    Accès libre
    High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling
    (2021-04-23T22:43:16Z)
    Hannes Eriksson
    ;
    Dimitrakakis, Christos 
    ;
    Lars Carlsson
    We study the problem of performing automated experiment design for drug screening through Bayesian inference and optimisation. In particular, we compare and contrast the behaviour of linear-Gaussian models and Gaussian processes, when used in conjunction with upper confidence bound algorithms, Thompson sampling, or bounded horizon tree search. We show that non-myopic sophisticated exploration techniques using sparse tree search have a distinct advantage over methods such as Thompson sampling or upper confidence bounds in this setting. We demonstrate the significant superiority of the approach over existing and synthetic datasets of drug toxicity.
  • Publication
    Accès libre
    SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning
    (2021-02-22T14:45:39Z)
    Hannes Eriksson
    ;
    Debabrota Basu
    ;
    Mina Alibeigi
    ;
    Dimitrakakis, Christos 
    In this paper, we consider risk-sensitive sequential decision-making in Reinforcement Learning (RL). Our contributions are two-fold. First, we introduce a novel and coherent quantification of risk, namely composite risk, which quantifies the joint effect of aleatory and epistemic risk during the learning process. Existing works considered either aleatory or epistemic risk individually, or as an additive combination. We prove that the additive formulation is a particular case of the composite risk when the epistemic risk measure is replaced with expectation. Thus, the composite risk is more sensitive to both aleatory and epistemic uncertainty than the individual and additive formulations. We also propose an algorithm, SENTINEL-K, based on ensemble bootstrapping and distributional RL for representing epistemic and aleatory uncertainty respectively. The ensemble of K learners uses Follow The Regularised Leader (FTRL) to aggregate the return distributions and obtain the composite risk. We experimentally verify that SENTINEL-K estimates the return distribution better, and while used with composite risk estimates, demonstrates higher risk-sensitive performance than state-of-the-art risk-sensitive and distributional RL algorithms.
  • Publication
    Accès libre
    Bayesian Reinforcement Learning via Deep, Sparse Sampling
    (2020)
    Divya Grover
    ;
    Debabrota Basu
    ;
    Dimitrakakis, Christos 
    We address the problem of Bayesian reinforcement learning using efficient model-based online planning. We propose an optimism-free Bayes-adaptive algorithm to induce deeper and sparser exploration with a theoretical bound on its performance relative to the Bayes optimal policy, with a lower computational complexity. The main novelty is the use of a candidate policy generator, to generate long-term options in the planning tree (over beliefs), which allows us to create much sparser and deeper trees. Experimental results on different environments show that in comparison to the state-of-the-art, our algorithm is both computationally more efficient, and obtains significantly higher reward in discrete environments.
  • Publication
    Accès libre
    Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities
    (2019)
    Aristide Tossou
    ;
    Debabrota Basu
    ;
    Dimitrakakis, Christos 
    We study model-based reinforcement learning in an unknown finite communicating Markov decision process. We propose a simple algorithm that leverages a variance based confidence interval. We show that the proposed algorithm, UCRL-V, achieves the optimal regret O~(DSAT−−−−−−√) up to logarithmic factors, and so our work closes a gap with the lower bound without additional assumptions on the MDP. We perform experiments in a variety of environments that validates the theoretical bounds as well as prove UCRL-V to be better than the state-of-the-art algorithms.
  • Publication
    Accès libre
    Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?
    (2019)
    Debabrota Basu
    ;
    Dimitrakakis, Christos 
    ;
    Aristide Tossou
    Based on differential privacy (DP) framework, we introduce and unify privacy definitions for the multi-armed bandit algorithms. We represent the framework with a unified graphical model and use it to connect privacy definitions. We derive and contrast lower bounds on the regret of bandit algorithms satisfying these definitions. We leverage a unified proving technique to achieve all the lower bounds. We show that for all of them, the learner's regret is increased by a multiplicative factor dependent on the privacy level ϵ. We observe that the dependency is weaker when we do not require local differential privacy for the rewards.
  • Publication
    Accès libre
    Thompson Sampling For Stochastic Bandits with Graph Feedback
    (2017-01-16T10:52:51Z)
    Aristide C. Y. Tossou
    ;
    Dimitrakakis, Christos 
    ;
    Devdatt Dubhashi
    We present a novel extension of Thompson Sampling for stochastic sequential decision problems with graph feedback, even when the graph structure itself is unknown and/or changing. We provide theoretical guarantees on the Bayesian regret of the algorithm, linking its performance to the underlying properties of the graph. Thompson Sampling has the advantage of being applicable without the need to construct complicated upper confidence bounds for different problems. We illustrate its performance through extensive experimental results on real and simulated networks with graph feedback. More specifically, we tested our algorithms on power law, planted partitions and Erdo's-Renyi graphs, as well as on graphs derived from Facebook and Flixster data. These all show that our algorithms clearly outperform related methods that employ upper confidence bounds, even if the latter use more information about the graph.