In multi-agent coverage control problems, agents navigate their environm...
Learning optimal control policies directly on physical systems is challe...
When learning policies for robotic systems from data, safety is a major
...
For many reinforcement learning (RL) applications, specifying a reward i...
Adaptive control approaches yield high-performance controllers when a pr...
In Interactive Machine Learning (IML), we iteratively make decisions and...
In reinforcement learning (RL), an autonomous agent learns to perform co...
The optimization of expensive to evaluate, black-box, mixed-variable
fun...
This work presents a learning-based approach for target driven map-less
...
Learning-based methods have been successful in solving complex control t...
Reinforcement learning is a powerful paradigm for learning optimal polic...
In classical reinforcement learning, when exploring an environment, agen...