Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds

We describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement learning to model the confidence and consistency of human feedback. This enables deep reinforcement learning algorithms to determine the most appropriate time to listen to the human feedback, exploit the current policy model, or explore the agent's environment. Managing the trade-off between these three strategies allows DRL agents to be robust to inconsistent or intermittent human feedback. Through experimentation using a synthetic oracle, we show that our technique improves the training speed and overall performance of deep reinforcement learning in navigating three-dimensional environments using Minecraft. We further show that our technique is robust to highly innacurate human feedback and can also operate when no human feedback is given

Snow crab recapture data 2006-2008, Conception Bay

Snow crabs from Conception Bay that were recaptured after a tagging study initiated in 2006. DAL= days at large, M= disease status at tagging (0=healthy, 1=diseased), disease (bitter crab disease) was diagnosed by visual assessment of the carapace. Data on crabs that were not recaptured is excluded

American lobster Recapture data- LIS - Epizootic shell disease

Data on recaptures only from American lobsters tagged and recaptured by the Millstone Environmental Lab in Millstone, CT. Data file includes: tag_num: unique tag identifier, rel_month: month of tagging, rel_day: day of tagging, rel_year: year of tagging, sex, rel_carapace_length: carapace length at tagging, male:1 if male, ovig: 1 if ovigerous female at tagging, female: 1 if non-ovigerous female at tagging, dal: days at large between tagging and release, disease_status: disease status at tagging (0 is healthy, 1 is mildly diseased (<10% of carapace with lesions), 2 is moderately diseased (10-50% of carapace with lesions), 3 is severely diseased (>50% of carapace with lesions)), recap_date: date a recapture. Data on animals that were tagged and not recaptured or were recaptured in other locations are not included in this dataset