In this work, we consider the popular tree-based search strategy within the framework of
reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of infinite …

One of the challenges in online reinforcement learning (RL) is that the agent needs to trade
off the exploration of the environment and the exploitation of the samples to optimize its …

We propose SmoothCruiser, a new planning algorithm for estimating the value function in
entropy-regularized Markov decision processes and two-player games, given a generative …

Learning to reach goals is a competence of high practical relevance to acquire for intelligent
agents. For instance, this encompasses many navigation tasks (" go to target X"), robotic …

Reinforcement learning (RL) is a powerful machine learning framework to design algorithms
that learn to make decisions and to interact with the world. Algorithms for RL can be …

Ce sujet concerne l'optimisation de l'utilisation des ressources numériques telles que la
mémoire et le temps CPU pour améliorer les performances liées à la résolution de …

Reinforcement learning (RL) has recently emerged as a generic yet powerful solution for
learning complex decision-making policies, providing the key foundational underpinnings of …