Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication.
Emanuele PesceGiovanni MontanaPublished in: Mach. Learn. (2020)
Keyphrases
- small scale
- multi agent
- reinforcement learning
- agent communication
- multi agent reinforcement learning
- multi agent systems
- distributed control
- multiple agents
- cooperative multi agent systems
- multiagent systems
- cooperative
- multi agent coordination
- heterogeneous agents
- information sharing
- information exchange
- autonomous agents
- multi agent environments
- data driven
- single agent
- agent interactions
- interaction protocols
- function approximation
- multiagent learning
- state space
- multi agent planning
- learning agents
- larger scale
- reinforcement learning algorithms
- memory usage
- intelligent agents
- machine learning
- model free
- memory requirements
- communication systems
- decentralized control
- interprocess communication
- software agents
- reinforcement learning agents
- multiagent reinforcement learning
- open systems
- optimal policy
- action selection
- multi agent learning
- state information
- partially observable markov decision processes