This paper investigates the motion planning of autonomous dynamical syst...
Deep reinforcement learning (DRL) is applied in safety-critical domains ...
In Hierarchical Control, compositionality, abstraction, and task-transfe...
This paper presents the concept of an adaptive safe padding that forces
...
We propose a method for effective training of deep Reinforcement Learnin...
We propose a method for efficient training of deep Reinforcement Learnin...
We propose an actor-critic, model-free, and online Reinforcement Learnin...
Reinforcement Learning (RL) has emerged as an efficient method of choice...
This paper proposes the first model-free Reinforcement Learning (RL)
fra...
This paper proposes a method for efficient training of the Q-function fo...
Multi-agent Systems (MASs) have found a variety of industrial applicatio...
We propose a novel Reinforcement Learning (RL) algorithm to synthesize
p...