This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

The goal of meta-reinforcement learning (meta-RL) is to build agents that can quickly learn
new tasks by leveraging prior experience on related tasks. Learning a new task often …

Identifying statistical regularities in solutions to some tasks in multi-task reinforcement
learning can accelerate the learning of new tasks. Skill learning offers one way of identifying …

Develo** interactive software, such as websites or games, is a particularly engaging way
to learn computer science. However, teaching and giving feedback on such software is time …

In Reinforcement Learning (RL), most algorithms use a fixed reward function, and few
studies discuss transferring the reward function during learning. Actually, different types of …

Reinforcement learning from scratch often requires a tremendous number of samples to
learn complex tasks, but many real-world applications demand learning from only a few …

Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize
cumulative rewards through trial-and-error interactions with dynamic environments. In recent …

La mente es lo que diferencia a los seres humanos del resto de animales. Esta capacidad
de razonar, dialogar, pensar y sentir sólo ha sido encontrada en nosotros mismos. Estas …