This article focuses on the recent advances in the field of reinforcement learning (RL) as well
as the present state–of–the–art applications in games. First, we give a general panorama of …

Generalization is a central challenge for the deployment of reinforcement learning (RL)
systems in the real world. In this paper, we show that the sequential structure of the RL …

Standard deep reinforcement learning algorithms use a shared representation for the policy
and value function, especially when training directly from images. However, we argue that …

Deep reinforcement learning (RL) agents often fail to generalize beyond their training
environments. To alleviate this problem, recent work has proposed the use of data …

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

Generalization has been a long-standing challenge for reinforcement learning (RL). Visual
RL, in particular, can be easily distracted by irrelevant factors in high-dimensional …

In reinforcement learning, the epsilon (ε)-greedy strategy is commonly employed as an
exploration technique This method, however, leads to extensive initial exploration and …

Despite recent progress in offline learning, these methods are still trained and tested on the
same environment. In this paper, we compare the generalization abilities of widely used …

Considering autonomous navigation in busy marine traffic environments (including harbors
and coasts), major study issues to be solved for autonomous ships are avoidance of static …

Despite the recent progress in offline reinforcement learning (RL) algorithms, agents are
usually trained and tested on the same environment. In this paper, we perform an in-depth …