  We develop a simple framework to learn bio-inspired foraging policies using
human data. We conduct an experiment where humans are virtually immersed in an
open field foraging environment and are trained to collect the highest amount
of rewards. A Markov Decision Process (MDP) framework is introduced to model
the human decision dynamics. Then, Imitation Learning (IL) based on maximum
likelihood estimation is used to train Neural Networks (NN) that map human
decisions to observed states. The results show that passive imitation
substantially underperforms humans. We further refine the human-inspired
policies via Reinforcement Learning (RL) using the on-policy Proximal Policy
Optimization (PPO) algorithm which shows better stability than other algorithms
and can steadily improve the policies pretrained with IL. We show that the
combination of IL and RL can match human results and that good performance
strongly depends on combining the allocentric information with an egocentric
representation of the environment.
