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.

Implicit imitation assumes that learning agents observe only 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. In this paper, we put forward a deep implicit imitation Q-network (DIIQN) model, which incorporates ideas from three well-known Deep Q-Network (DQN) variants. As such, we enable a novel implicit imitation method for online, model-free deep reinforcement learning. Our thorough experimentation in the complex environment of the emerging lane-free traffic paradigm, verifies the benefits of our approach. Specifically, we show that deep implicit imitation RL dramatically accelerates the learning process when compared to a “vanilla” DQN method; and, unlike explicit imitation reinforcement learning, it is able to outperform mentor performance without resorting to additional information, such as the mentor’s actions.