Deep reinforcement learning (DRL) agents are often sensitive to visual changes that were
unseen in their training environments. To address this problem, we leverage the sequential …

Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been
in the limelight because of many recent breakthroughs in artificial intelligence, including …

New edition of the bestselling guide to deep reinforcement learning and how it's used to
solve complex real-world problems. Revised and expanded to include multi-agent methods …

Combining deep model-free reinforcement learning with on-line planning is a promising
approach to building on the successes of deep RL. On-line planning with look-ahead trees …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

Since the introduction of DQN, a vast majority of reinforcement learning research has
focused on reinforcement learning with deep neural networks as function approximators …

In recent years, a specific machine learning method called deep learning has gained huge
attraction, as it has obtained astonishing results in broad applications such as pattern …

Deep reinforcement learning algorithms have shown an impressive ability to learn complex
control policies in high-dimensional tasks. However, despite the ever-increasing …

STABLE-BASELINES3 provides open-source implementations of deep reinforcement
learning (RL) algorithms in Python. The implementations have been benchmarked against …