Biologically inspired reinforcement learning: Reward-based decomposition for multi-goal environments

We present an emotion-based hierarchical reinforcement learning (HRL) algorithm for environments with multiple sources of reward. The architecture of the system is inspired by the neurobiology of the brain and particularly those areas responsible for emotions, decision making and behaviour execution, being the amygdala, the orbito-frontal cortex and the basal ganglia respectively. The learning problem is decomposed according to sources of reward. A reward source serves as a goal for a given subtask. Each subtask is assigned an artificial emotion indication (AEI) which predicts the reward component associated with the subtask. The AEIs are learned along with the top-level policy simultaneously and used to interrupt subtask execution when the AEIs change significantly. The algorithm is tested in a simulated gridworld which has two sources of reward and is partially observable. Experiments are performed comparing the emotion based algorithm with other HRL algorithms under the same learning conditions. The use of the biologically inspired architecture significantly accelerates the learning process and achieves higher long term reward compared to a human designed policy and a restricted form of the MAXQ algorithm.