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Alain Dutech

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8 papers
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8

EWRL Workshop 2016 Workshop Paper

Toward a data efficient neural actor-critic

  • Matthieu Zimmer
  • Yann Boniface
  • Alain Dutech

A new off-policy, offline, model-free, actor-critic reinforcement learning algorithm dealing with continuous environments in both states and actions is presented. It addresses discrete time problems where the goal is to maximize the discounted sum of rewards using stationary policies. Our algorithm allows to trade-off between data-efficiency and scalability. The amount of a priori knowledge is kept low by: (1) using neural networks to learn both the critic and the actor, (2) not relying on initial trajectories provided by an expert, and (3) not depending on known goal states. Experimental results show better data-efficiency than 4 state-of-the-art algorithms on two benchmark environments.

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ECAI Conference 2014 Conference Paper

Simultaneous Tracking and Activity Recognition (STAR) using Advanced Agent-Based Behavioral Simulations

  • Arsène Fansi Tchango
  • Vincent Thomas
  • Olivier Buffet
  • Fabien Flacher
  • Alain Dutech

Tracking and understanding moving pedestrian behaviors is of major concern for a growing number of applications. Classical approaches either consider both problems separately or treat them simultaneously on the basis of limited contextual graphical models. In this paper, we consider tackling both problems jointly based on richer contextual information issued from agent-based behavioral simulators designed for realistically reproducing human behaviors within complex environments. We focus on the single target case and experimentally show that the proposed approach keeps good performances even in case of long periods of occlusion.

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ICAPS Conference 2007 Conference Paper

Mixed Integer Linear Programming for Exact Finite-Horizon Planning in Decentralized Pomdps

  • Raghav Aras
  • Alain Dutech
  • François Charpillet

We consider the problem of finding an n-agent joint-policy for the optimal finite-horizon control of a decentralized Pomdp (Dec-Pomdp). This is a problem of very high complexity (NEXP-hard in n ≥ 2). In this paper, we propose a new mathematical programming approach for the problem. Our approach is based on two ideas: First, we represent each agent's policy in the sequence-form and not in the tree-form, thereby obtaining a very compact representation of the set of joint-policies. Second, using this compact representation, we solve this problem as an instance of combinatorial optimization for which we formulate a mixed integer linear program (MILP). The optimal solution of the MILP directly yields an optimal joint-policy for the Dec-Pomdp. Computational experience shows that formulating and solving the MILP requires significantly less time to solve benchmark Dec-Pomdp problems than existing algorithms. For example, the multi-agent tiger problem for horizon 4 is solved in 72 secs with the MILP whereas existing algorithms require several hours to solve it.

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JAAMAS Journal 2007 Journal Article

Shaping multi-agent systems with gradient reinforcement learning

  • Olivier Buffet
  • Alain Dutech
  • François Charpillet

Abstract An original reinforcement learning (RL) methodology is proposed for the design of multi-agent systems. In the realistic setting of situated agents with local perception, the task of automatically building a coordinated system is of crucial importance. To that end, we design simple reactive agents in a decentralized way as independent learners. But to cope with the difficulties inherent to RL used in that framework, we have developed an incremental learning algorithm where agents face a sequence of progressively more complex tasks. We illustrate this general framework by computer experiments where agents have to coordinate to reach a global goal.

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v2026.09.13