RL GAMMA ZERO

Reinforcement Learning (RL) studies the problem of sequential decision-making when the environment (i.e., the dynamics and the reward) is initially unknown but can be learned through direct interaction. RL algorithms recently achieved impressive results in a variety of problems including games and robotics... Nonetheless, most of recent RL algorithms require a huge amount of data to learn a satisfactory policy and cannot be used in domains where samples are expensive and/or long simulations are not possible (e.g., human-computer interaction). A fundamental step towards more sample-efficient algorithms is to devise methods to properly balance the exploration of the environment, to gather useful information, and the exploitation of the learned policy to collect as much reward as possible... The objective of the tutorial is to bring awareness of the importance of the exploration-exploitation dilemma in improving the sample-efficiency of modern RL algorithms. The tutorial will provide..