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Abstract:
We report on the use of reinforcement learning with Cobot, a
software agent residing in the well-known online community
LambdaMOO. Our initial work on Cobot (Isbell et al. 2000)
provided him with the ability to collect social statistics and
report them to users. Here we describe an application of RL
allowing Cobot to take proactive actions in this complex social
environment, and adapt behavior from multiple sources of human
reward. After 5 months of training, and 3171 reward and
punishment events from 254 different LambdaMOO users, Cobot
learned nontrivial preferences for a number of users, modifing
his behavior based on his current state. Here we describe
LambdaMOO and the state and action spaces of Cobot, and report
the statistical results of the learning experiment.
References
Isbell, C. L., Kearns, M., Kormann, D., Singh, S., and Stone,
P. (2000). Cobot in LambdaMOO: A social statistics agent. To
appear in
Proceedings of AAAI-2000
.
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