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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
    Adaptive Belief Discretization for POMDP Planning
    (2021-04-15T07:04:32Z)
    Divya Grover
    ;
    Dimitrakakis, Christos 
    Partially Observable Markov Decision Processes (POMDP) is a widely used model to represent the interaction of an environment and an agent, under state uncertainty. Since the agent does not observe the environment state, its uncertainty is typically represented through a probabilistic belief. While the set of possible beliefs is infinite, making exact planning intractable, the belief space's complexity (and hence planning complexity) is characterized by its covering number. Many POMDP solvers uniformly discretize the belief space and give the planning error in terms of the (typically unknown) covering number. We instead propose an adaptive belief discretization scheme, and give its associated planning error. We furthermore characterize the covering number with respect to the POMDP parameters. This allows us to specify the exact memory requirements on the planner, needed to bound the value function error. We then propose a novel, computationally efficient solver using this scheme. We demonstrate that our algorithm is highly competitive with the state of the art in a variety of scenarios.
  • 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.