Model-based reinforcement learning promises to learn an optimal policy from fewer
interactions with the environment compared to model-free reinforcement learning by …

With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

Simple and efficient exploration remains a core challenge in deep reinforcement learning.
While many exploration methods can be applied to high-dimensional tasks, these methods …

Abstract In recent years, Reinforcement Learning (RL), and especially Deep RL (DRL), have
shown outstanding performance in video games from Atari, Mario, to StarCraft. Most of the …

Existing approaches for improving generalization in deep reinforcement learning (RL) have
mostly focused on representation learning, neglecting RL-specific aspects such as …

Deep reinforcement learning (RL) has become one of the most popular topics in artificial
intelligence research. It has been widely used in various fields, such as end-to-end control …

Deep reinforcement learning (DRL) is capable of learning high-performing policies on a
variety of complex high-dimensional tasks, ranging from video games to robotic …

Success stories of applied machine learning can be traced back to the datasets and
environments that were put forward as challenges for the community. The challenge that the …

This work focuses on the sample collection in reinforcement learning (RL), where the
interaction with the environment is typically time-consuming and extravagantly expensive. In …

Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet
most state-of-the-art algorithms use a naive exploration protocol like $\epsilon $-greedy …