The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions; especially considering the emergent field of deep RL (DRL). However a …

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

Exploration in reinforcement learning is, in general, a challenging problem. A common
technique to make learning easier is providing demonstrations from a human supervisor, but …

Spontaneous, volitional spatial exploration is crucial for building up a cognitive map of the
environment. However, decades of research have primarily measured the fidelity of …

To successfully learn real-life behavioral tasks, animals must pair actions or decisions to the
task's complex structure, which can depend on abstract combinations of sensory stimuli and …

A significant challenge in modern computer security is the growing skill gap as intruder
capabilities increase, making it necessary to begin automating elements of penetration …

Safety is critical to broadening the application of reinforcement learning (RL). Often, we train
RL agents in a controlled environment, such as a laboratory, before deploying them in the …

Humans and animals explore their environment and acquire useful skills even in the
absence of clear goals, exhibiting intrinsic motivation. The study of intrinsic motivation in …

Deep reinforcement learning (DRL) techniques have been used to solve a discretionary
lane change decision-making problem and are showing promising results. However, since …