Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning.
Adil KhanFeng JiangShaohui LiuMuhammad Zubair AsgharPublished in: J. Robotics (2019)
Keyphrases
- fine grained
- reinforcement learning
- optimal control
- coarse grained
- multi agent
- control policy
- action selection
- control problems
- access control
- multi agent systems
- dynamic programming
- learning agents
- multi agent reinforcement learning
- software agents
- multiple agents
- multiagent systems
- learning agent
- decentralized control
- agent receives
- tightly coupled
- robot control
- single agent
- control system
- massively parallel
- autonomous agents
- mobile agents
- machine learning
- dynamic environments
- robocup soccer