Show me the Way: Intrinsic Motivation from Demonstrations
L\'eonard Hussenot, Robert Dadashi, Matthieu Geist, Olivier Pietquin

TL;DR
This paper proposes a method to learn exploration bonuses from demonstrations, enabling artificial agents to adopt complex, human-like motivations for exploration, which improves their ability to learn in environments where exhaustive exploration is impractical.
Contribution
It introduces an inverse reinforcement learning approach to derive exploration incentives from demonstrations, capturing diverse motivations beyond novelty for more effective exploration.
Findings
Agents can learn complex exploration behaviors from demonstrations.
The learned exploration bonuses improve performance in challenging environments.
The approach reduces the need for exhaustive exploration in RL tasks.
Abstract
The study of exploration in the domain of decision making has a long history but remains actively debated. From the vast literature that addressed this topic for decades under various points of view (e.g., developmental psychology, experimental design, artificial intelligence), intrinsic motivation emerged as a concept that can practically be transferred to artificial agents. Especially, in the recent field of Deep Reinforcement Learning (RL), agents implement such a concept (mainly using a novelty argument) in the shape of an exploration bonus, added to the task reward, that encourages visiting the whole environment. This approach is supported by the large amount of theory on RL for which convergence to optimality assumes exhaustive exploration. Yet, Human Beings and mammals do not exhaustively explore the world and their motivation is not only based on novelty but also on various…
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Taxonomy
TopicsSocial Media and Politics
