Deep Implicit Imitation Reinforcement Learning in Heterogeneous Action Settings - Supplementary Material

Implicit imitation reinforcement learning (IIRL) is a framework that aims to aid a trainee agent’s learning process via observing the state transitions of a mentor, but without access to the latter's action information. Standard IIRL assumes a shared Markov decision process (MDP) between the mentor and trainee, consequently implying an identical action space. This restriction imposes limitations on the applicability of implicit imitation frameworks in real-life scenarios where, possibly due to variations in physical characteristics, the mentor agent may possess distinct own actions, thereby creating a heterogeneous action setting. In this work, we extend the deep implicit imitation Q-networks (DIIQN) method -an online, model-free, deep RL algorithm for implicit imitation- to allow for heterogeneous action sets between mentor and trainee agents. Equipped with our heterogeneous actions DIIQN (HA-DIIQN) method, a trainee agent can harvest the benefits of IIRL even in heterogeneous action settings, achieving accelerated learning and outperforming non-optimal mentor agents. The research described in this paper was carried out within the framework of the National Recovery and Resilience Plan Greece 2.0, funded by the European Union - NextGenerationEU (Implementation Body: HFRI. Project name: DEEP-REBAYES. HFRI Project Number 15430). Moreover, the research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation programme/ERC Grant Agreement n.[833915], project TrafficFluid

Deep Implicit Imitation Reinforcement Learning in Heterogeneous Action Settings - Supplementary Material

Implicit imitation reinforcement learning (IIRL) is a framework that aims to aid a trainee agent’s learning process via observing the state transitions of a mentor, but without access to the latter's action information. Standard IIRL assumes a shared Markov decision process (MDP) between the mentor and trainee, consequently implying an identical action space. This restriction imposes limitations on the applicability of implicit imitation frameworks in real-life scenarios where, possibly due to variations in physical characteristics, the mentor agent may possess distinct own actions, thereby creating a heterogeneous action setting. In this work, we extend the deep implicit imitation Q-networks (DIIQN) method -an online, model-free, deep RL algorithm for implicit imitation- to allow for heterogeneous action sets between mentor and trainee agents. Equipped with our heterogeneous actions DIIQN (HA-DIIQN) method, a trainee agent can harvest the benefits of IIRL even in heterogeneous action settings, achieving accelerated learning and outperforming non-optimal mentor agents. The research described in this paper was carried out within the framework of the National Recovery and Resilience Plan Greece 2.0, funded by the European Union - NextGenerationEU (Implementation Body: HFRI. Project name: DEEP-REBAYES. HFRI Project Number 15430). Moreover, the research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation programme/ERC Grant Agreement n.[833915], project TrafficFluid

Deep Implicit Imitation Reinforcement Learning in Heterogeneous Action Settings

Implicit imitation reinforcement learning (IIRL) is a framework that aims to aid a trainee agent’s learning process via observing the state transitions of a mentor, but without access to the latter's action information. Standard IIRL assumes a shared Markov decision process (MDP) between the mentor and trainee, consequently implying an identical action space. This restriction imposes limitations on the applicability of implicit imitation frameworks in real-life scenarios where, possibly due to variations in physical characteristics, the mentor agent may possess distinct own actions, thereby creating a heterogeneous action setting. In this work, we extend the deep implicit imitation Q-networks (DIIQN) method -an online, model-free, deep RL algorithm for implicit imitation- to allow for heterogeneous action sets between mentor and trainee agents. Equipped with our heterogeneous actions DIIQN (HA-DIIQN) method, a trainee agent can harvest the benefits of IIRL even in heterogeneous action settings, achieving accelerated learning and outperforming non-optimal mentor agents

Βαθιά υπό καθοδήγηση ενισχυτική μάθηση

Summarization: Imitation is a popular technique of behavioral learning widely practiced in nature. The most famous applications involve animal babies imitating their parents, with imitation providing the stepping stone to walk their first steps in life survival. Additionally, imitation examples can be found in cross species instances, with most known samples the voice imitation of parrots or crow behavioral imitation. The imitation learning paradigm has naturally been taken up in machine learning applications, implemented in supervised learning and in reinforcement learning, mostly with the use of explicit imitation, where the mentor agent attempts to explicitly teach learners. Implicit imitation, on the other hand, assumes that learning agents observe the state transitions of an agent they use as a mentor, and try to recreate them based on their own abilities and knowledge of their environment. Though it has also been employed with some success in the past, implicit imitation has only recently been utilized in conjunction with deep reinforcement learning, the current leading reinforcement learning paradigm. In this thesis, we enhance the operation of implicit imitation by adding four state-of-the-art deep reinforcement learning algorithms, treated as "imitation optimization modules". These include Double Deep Q-network [Hasselt, Guez, and Silver, 2016], Prioritized Experience Replay [Schaul et al., 2016], Dueling Network Architecture [Wang et al., 2016] and Parameter Space Noise for Exploration [Plappert et al., 2018]. We modify these appropriately to better fit the implicit imitation learning paradigm. By enabling and disabling those methods we create diverse combinations of them; systematically test and compare the viability of each one of these combinations; and end up with a clear "winner": the combination of Double Deep Q-network, Prioritized Experience Replay and Dueling Network Architecture.Περίληψη: Η μίμηση αποτελεί μία τεχνική συμπεριφορικής εκμάθησης, ευρέως χρησιμοποιούμενη στην φύση. Στο ζωϊκό βασίλειο, για παράδειγμα, τα μωρά μιμούνται τους γονείς τους, και η μίμηση τα εφοδιάζει με τις κατάλληλες γνώσεις για να περπατήσουν στα πρώτα τους βήματα επιβίωσης. Παραδείγματα μίμησης παρατηρούνται και μεταξύ διαφορετικών ειδών, όπως στην φωνητική μίμηση των παπαγάλων ή στην συμπεριφορική μίμηση των κορακιών. Η μίμηση, λοιπόν, δεν θα μπορούσε να μην συμπεριληφθεί σε εφαρμογές της μηχανικής μάθησης, όπου αλγόριθμοι των πεδίων επιτηρούμενης μάθησης και ενισχυτικής μάθησης εκμεταλλεύονται την χρήση τεχνικών απευθείας, κυρίως, μίμησης, όπου ο πράκτορας που λειτουργεί ως "μέντορας'' προσπαθεί να "διδάξει'' απευθείας άλλους. Η μηχανική εκμάθηση μέσω έμμεσης μίμησης, από την άλλη, θεωρεί ότι οι πράκτορες-μιμητές απλά παρατηρούν τις αλλαγές καταστάσεων που προκύπτουν από την συμπεριφορά ενός πράκτορα που επιλέγουν ως μέντορα, και προσπαθούν να τις αναπαράγουν με βάση τις δικές τους δυνατότητες και γνώση του περιβάλλοντός τους. Αν και η έμμεση μίμηση έχει χρησιμοποιηθεί με ικανοποιητικά αποτελέσματα στο απώτερο παρελθόν, μόλις πρόσφατα έχει αξιοποιηθεί σε συνδυασμό με βαθιά ενισχυτική μάθηση, η οποία αποτελεί μια τρέχουσα τεχνολογία αιχμής στη μηχανική μάθηση. Στην παρούσα διπλωματική εργασία, βελτιώνουμε περαιτέρω την διαδικασία της έμμεσης μηχανικής εκμάθησης ενσωματώνοντας τέσσερις σύγχρονους αλγόριθμους βαθειάς ενισχυτικής μάθησης, τους οποίους θεωρούμε και χρησιμοποιούμε ως δομικά στοιχεία βελτιστοποίησης της προσπάθειας μίμησης. Οι εν λόγω αλγόριθμοι είναι οι Double Deep Q-network [Hasselt, Guez, and Silver, 2016], Prioritized Experience Replay [Schaul et al., 2016], Dueling Network Architecture [Wang et al., 2016] και Parameter Space Noise for Exploration [Plappert et al., 2018]. Προσαρμόσαμε τη λειτουργία των αλγορίθμων ώστε να συνάδει με το μοντέλο της έμμεσης μίμησης. Ενεργοποιώντας και απενεργοποιώντας τις παραπάνω μεθόδους, δημιουργούμε ποικίλους συνδυασμούς αυτών, και δοκιμάζουμε μεθοδικά και συγκρίνουμε την βιωσιμότητα του κάθε ενός από αυτούς τους συνδυασμούς. Οι πειραματισμοί μας κατέληξαν στην ανάδειξη ενός ξεκάθαρου "νικητή”: συγκεκριμένα, του συνδυασμού των Double Deep Q-network, Prioritized Experience Replay και Dueling Network Architecture