Topic: value function

In order to thrive in complex environments, animals and artificial agents have to learn to act adaptively to maximize fitness and rewards. Reinforcement learning (RL) algorithms have been successful at training artificial agents to superhuman performance in an array of complex tasks and the activity of dopaminergic neurons has been described by one of their key components, the reward prediction error (RPE). However, classical RL occurs at a single timescale, set by the discount factor, while agents have to make decisions at multiple timescales and behaviorally, humans and animals are known to