Promoting Behavioral Diversity via Multi-Objective/Quality-Diversity Novelty Producing Synaptic Plasticity

Learning in contexts where measuring the performance of the agents is either impossible or misleading requires different approaches in the search of the solution. These problems require either a complete exploration of the search space, or the use of reward-independent approaches, which may not be feasible in some situations. The Novelty Producing Synaptic Plasticity (NPSP) algorithm was recently proposed as a means to obtain successful learning in such contexts, by evolving synaptic plasticity rules able to generate as many novel behaviors as possible. Here, we consider a deceptive maze navigation task and extend the NPSP paradigm to a multi-objective case, by applying NSGA2 to maximizing a goal-agnostic metric (novelty) while minimizing a goal-aware metric (distance), in order to find the possible trade-offs. We then introduce an additional goal-agnostic metric (exploration) and apply MAP-Elites to “illuminate” the feature space projected by novelty and exploration. Lastly, we consider modified settings where 1) sensors are affected by random noise, and 2) the sensor perception is augmented, in order to assess the generalizability of the evolved synaptic rules across settings. Overall, our results show that both multi-objective and MAP-Elites based NPSP can find successful solutions in the different settings of the task.