A Sample Efficient Multi-Agent Approach to Continuous Reinforcement Learning
Author:
Affiliation:
1. KTH Royal Institute of Technology,Ericsson AB and Software and Computer Systems,Stockholm,Sweden
2. Research Institutes of Sweden,RISE AI,Kista,Sweden
3. KTH Royal Institute of Technology,Software and Computer Systems,Stockholm,Sweden
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9964474/9964490/09965060.pdf?arnumber=9965060
Reference24 articles.
1. Using communication to reduce locality in distributed multiagent learning
2. A new look at Bellman's principle of optimality
3. Simple statistical gradient-following algorithms for connectionist reinforcement learning
4. Policy gradient methods for reinforcement learning with function approximation;sutton;Proc NIPS 12,2000
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