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

Average number of SNPs per kilobase pair in 152 contigs associated with GO Slim biological processes.

<p>Bar heights represent the average SNP rate per kilobase pair for select GO Slim biological processes. Color intensity of the bars indicates number of contigs for each GO Slim term.</p

Classification of annotated QPX contigs based on Gene Ontology.

<p>Representation of (a) biological processes, (b) molecular function, and (c) cellular components from Gene Ontology Slim terms based on Swiss-Prot gene annotations. The gene ontology categories ‘other biological processes functions’ (a), ‘other molecular functions’ (b), and ‘other cellular components’ (c) were excluded from these graphs.</p

Relative gene expression levels (RPKM) between QPX10 and QPX21 libraries.

<p>Each circle represents a single contig, with blue circles indicating those contigs that are differentially expressed. The diagonal line represents equal expression between the two libraries.</p

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