Parallel Reinforcement Learning
نویسنده
چکیده
We examine the dynamics of multiple reinforcement learning agents who are interacting with and learning from the same environment in parallel. Due to the stochasticity of the environment, each agent will have a different learning experience though they should all ultimately converge upon the same value function. The agents can accelerate the learning process by sharing information at periodic points during the learning process.
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تاریخ انتشار 2002