Jilles Steeve Dibangoye

JAIR Journal 2016 Journal Article

Optimally Solving Dec-POMDPs as Continuous-State MDPs

• Jilles Steeve Dibangoye
• Christopher Amato
• Olivier Buffet
• François Charpillet

Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a general model for decision-making under uncertainty in decentralized settings, but are difficult to solve optimally (NEXP-Complete). As a new way of solving these problems, we introduce the idea of transforming a Dec-POMDP into a continuous-state deterministic MDP with a piecewise-linear and convex value function. This approach makes use of the fact that planning can be accomplished in a centralized offline manner, while execution can still be decentralized. This new Dec-POMDP formulation, which we call an occupancy MDP, allows powerful POMDP and continuous-state MDP methods to be used for the first time. To provide scalability, we refine this approach by combining heuristic search and compact representations that exploit the structure present in multi-agent domains, without losing the ability to converge to an optimal solution. In particular, we introduce a feature-based heuristic search value iteration (FB-HSVI) algorithm that relies on feature-based compact representations, point-based updates and efficient action selection. A theoretical analysis demonstrates that FB-HSVI terminates in finite time with an optimal solution. We include an extensive empirical analysis using well-known benchmarks, thereby demonstrating that our approach provides significant scalability improvements compared to the state of the art.

IJCAI Conference 2015 Conference Paper

Exploiting Separability in Multiagent Planning with Continuous-State MDPs (Extended Abstract)

• Jilles Steeve Dibangoye
• Christopher Amato
• Olivier Buffet
• Fran
• ccedil; ois Charpillet

Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a general model for decision-making under uncertainty in cooperative decentralized settings, but are difficult to solve optimally (NEXP-Complete). As a new way of solving these problems, we recently introduced a method for transforming a Dec-POMDP into a continuous-state deterministic MDP with a piecewise-linear and convex value function. This new Dec-POMDP formulation, which we call an occupancy MDP, allows powerful POMDP and continuous-state MDP methods to be used for the first time. However, scalability remains limited when the number of agents or problem variables becomes large. In this paper, we show that, under certain separability conditions of the optimal value function, the scalability of this approach can increase considerably. This separability is present when there is locality of interaction between agents, which can be exploited to improve performance. Unlike most previous methods, the novel continuous-state MDP algorithm retains optimality and convergence guarantees. Results show that the extension using separability can scale to a large number of agents and domain variables while maintaining optimality.

IJCAI Conference 2015 Conference Paper

Structural Results for Cooperative Decentralized Control Models

• Jilles Steeve Dibangoye
• Olivier Buffet
• Olivier Simonin

The intractability in cooperative, decentralized control models is mainly due to prohibitive memory requirements in both optimal policies and value functions. The complexity analysis has emerged as the standard method to estimating the memory needed for solving a given computational problem, but complexity results may be somewhat limited. This paper introduces a general methodology— structural analysis—for the design of optimalitypreserving concise policies and value functions, which will eventually lead to the development of efficient theory and algorithms. For the first time, we show that memory requirements for policies and value functions may be asymmetric, resulting in cooperative, decentralized control models with exponential reductions in memory requirements.

IJCAI Conference 2013 Conference Paper

Optimally Solving Dec-POMDPs as Continuous-State MDPs

• Jilles Steeve Dibangoye
• Christopher Amato
• Olivier Buffet
• François Charpillet

Optimally solving decentralized partially observable Markov decision processes (Dec-POMDPs) is a hard combinatorial problem. Current algorithms search through the space of full histories for each agent. Because of the doubly exponential growth in the number of policies in this space as the planning horizon increases, these methods quickly become intractable. However, in real world problems, computing policies over the full history space is often unnecessary. True histories experienced by the agents often lie near a structured, low-dimensional manifold embedded into the history space. We show that by transforming a Dec-POMDP into a continuous-state MDP, we are able to find and exploit these low-dimensional representations. Using this novel transformation, we can then apply powerful techniques for solving POMDPs and continuous-state MDPs. By combining a general search algorithm and dimension reduction based on feature selection, we introduce a novel approach to optimally solve problems with significantly longer planning horizons than previous methods.

IJCAI Conference 2009 Conference Paper

• Jilles Steeve Dibangoye
• Guy Shani
• Brahim Chaib-draa
• Abdel-illah Mouaddib

Over the past few years, point-based POMDP solvers scaled up to produce approximate solutions to mid-sized domains. However, to solve real world problems, solvers must exploit the structure of the domain. In this paper we focus on the topological structure of the problem, where the state space contains layers of states. We present here the Topological Order Planner (TOP) that utilizes the topological structure of the domain to compute belief space trajectories. TOP rapidly produces trajectories focused on the solveable regions of the belief space, thus reducing the number of redundant backups considerably. We demonstrate TOP to produce good quality policies faster than any other pointbased algorithm on domains with sufficient structure.