  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 and partially model-based
reinforcement learning approaches for several multiagent settings. In the realm
of normative reinforcement learning, I introduce scalable extensions to Monte
Carlo exploring starts for partially observable Markov Decision Processes
(POMDP), dubbed MCES-P, where I expand the theory and algorithm to the
multiagent setting. I first examine MCES-P with probably approximately correct
(PAC) bounds in the context of multiagent setting, showing MCESP+PAC holds in
the presence of other agents. I then propose a more sample-efficient
methodology for antagonistic settings, MCESIP+PAC. For cooperative settings, I
extend MCES-P to the Multiagent POMDP, dubbed MCESMP+PAC. I then explore the
use of reinforcement learning as a methodology in searching for optima in
realistic and latent model environments. First, I explore a parameterized
Q-learning approach in modeling humans learning to reason in an uncertain,
multiagent environment. Next, I propose an implementation of MCES-P, along with
image segmentation, to create an adaptive team-based reinforcement learning
technique to positively identify the presence of phenotypically-expressed water
and pathogen stress in crop fields.
