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 …
K Li, A Gupta, A Reddy, VH Pong… - International …, 2021 - proceedings.mlr.press
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 …
I Cone, C Clopath - Nature Communications, 2024 - nature.com
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 …
K Pozdniakov, E Alonso, V Stankovic… - … conference on cyber …, 2020 - ieeexplore.ieee.org
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 …