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Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this...
The reinforcement learning framework provides a simple way to study computational intelligence as the interaction between an agent and an environment. The goal of an agent is to accrue as much reward as possible by intelligently choosing actions given states. This problem of finding a policy that...