A l’occasion du quatre-centième anniversaire de la naissance de Molière (1622-1673), nous présentons un agent conversationnel qui parle comme un personnage du théâtre de Molière.
We describe briefly the concept of reinforcement learning algorithms and some approaches designed to date.
We demonstrate how Monte Carlo Search (MCS) algorithms, namely Nested Monte Carlo Search (NMCS) and Nested Rollout Policy Adaptation (NRPA), can be used to build graphs and find counter-examples to spectral graph theory conjectures in minutes.
Moreover, we leverage a graph neural network as a heuristic for tree search guidance.
In this paper we present an extension of the Nested Rollout Policy Adaptation algorithm (NRPA), namely the Generalized Nested Rollout Policy Adaptation (GNRPA), as well as its use for solving some instances of the Vehicle Routing Problem.
The uncertain criterion represents the feasibility of driving through the path without requiring human intervention.
In this paper, we focus on shortest path search with mandatory nodes on a given connected graph.
More specifically, a graph neural network is used to assist the branch and bound algorithm in handling constraints associated with a desired solution path.
Scheduling in the presence of uncertainty is an area of interest in artificial intelligence due to the large number of applications.
The standard for Deep Reinforcement Learning in games, following Alpha Zero, is to use residual networks and to increase the depth of the network to get better results.
The architecture of the neural networks used in Deep Reinforcement Learning programs such as Alpha Zero or Polygames has been shown to have a great impact on the performances of the resulting playing engines.
no code implementations • 27 Jan 2020 • Tristan Cazenave, Yen-Chi Chen, Guan-Wei Chen, Shi-Yu Chen, Xian-Dong Chiu, Julien Dehos, Maria Elsa, Qucheng Gong, Hengyuan Hu, Vasil Khalidov, Cheng-Ling Li, Hsin-I Lin, Yu-Jin Lin, Xavier Martinet, Vegard Mella, Jeremy Rapin, Baptiste Roziere, Gabriel Synnaeve, Fabien Teytaud, Olivier Teytaud, Shi-Cheng Ye, Yi-Jun Ye, Shi-Jim Yen, Sergey Zagoruyko
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games.