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Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning
[article]
2019
arXiv
pre-print
Optimal decision making with limited or no information in stochastic environments where multiple agents interact is a challenging topic in the realm of artificial intelligence. Reinforcement learning (RL) is a popular approach for arriving at optimal strategies by predicating stimuli, such as the reward for following a strategy, on experience. RL is heavily explored in the single-agent context, but is a nascent concept in multiagent problems. To this end, I propose several principled model-free
arXiv:1901.01325v1
fatcat:36i4dwlgfngzfi4bzlvfpnurze