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Table of contents
Abstract
Résumé
1 Introduction
1.1 Motivations
1.1.1 A short history of RL
1.1.2 Limitations of RL and deep RL
1.1.3 Definition and challenges of developmental robotics
1.1.4 The origins of developmental robotics
1.1.5 Developmental robotics and RL
1.2 Dissertation focus
1.2.1 Towards developmental interactive RL
1.2.2 Towards task-independent RL
1.2.3 Conclusion
1.3 Outline of the dissertation
1.3.1 Attention coordination and novelty search for task solving
1.3.2 Intentionality and competence progress for control
1.3.3 Competence progress and imitation learning for object manipulation
1.3.4 Discussion and conclusion
2 Combining novelty search and gaze-following
2.1 Background
2.1.1 Reinforcement learning background
2.1.2 Texplore
2.2 Methods
2.2.1 Environment and attention-action coordination
2.2.2 Gaze following motivation
2.2.3 Final algorithm
2.3 Experiments on task-oriented exploration
2.4 Reward-free environment
2.4.1 Experiments
2.5 Analysis of the results
2.5.1 Globally related work
2.5.2 A first limitation
2.6 Conclusion
3 Goal-directed learning
3.1 Goal-conditioned RL
3.1.1 RL and deep RL
3.1.2 Goal-conditioned RL
3.1.3 Goal-conditioned RL as multi-task RL
3.2 Curriculum learning
3.2.1 The Learning Progress Hypothesis
3.2.2 SAGG-RIAC plus goal-conditioned RL
3.2.3 Curriculum learning: related work
3.2.4 Limitation of an analysis
3.2.5 A theoretical standpoint
3.2.6 Conclusions
3.3 Accuracy-based curriculum learning in RL for control
3.3.1 Motivations
3.3.2 Methods
3.3.3 Results
3.3.4 Discussion
3.4 Conclusions
4 Curriculum Learning for Imitation and Control
4.1 Multi-task, multi-goal learning
4.1.1 CURIOUS
4.1.2 Limitations
4.2 CLIC
4.2.1 Motivations
4.2.2 Outline
4.3 Environments
4.3.1 Modeling objects
4.3.2 A second agent Bob
4.3.3 Environments instances
4.4 Methods
4.4.1 Multi-object control
4.4.2 Single-object control
4.4.3 Imitation
4.4.4 Curriculum learning
4.4.5 Summary and parameters
4.5 Results
4.5.1 Imitating Bob
4.5.2 Following Bob’s teaching
4.5.3 Ignoring non-reproducible behaviors from Bob
4.5.4 Ignoring Bob’s demonstrations for mastered objects
4.6 Analysis
4.6.1 Related work: Socially Guided Intrinsic Motivation
4.6.2 Related work: other approaches
4.7 Conclusion
5 Discussion
5.1 Contributions
5.1.1 A systemic approach to intelligent agents
5.1.2 Intrinsic motivation, attention synchrony and RL
5.1.3 Intention generation, intention selection and RL
5.1.4 Object control, intention selection, imitation and RL
5.2 Environments: limitations and perspectives
5.2.1 Arguments for ad hoc simple environments
5.2.2 Limitations
5.2.3 Perspectives
5.3 Agents: limitations and perspectives
5.3.1 Off-policy learning biases
5.3.2 Interaction limitations and perspectives
5.4 A discussion on objects
5.4.1 Object control: desired properties
5.4.2 Existing models
5.5 Environment control: a meta-RL point of view
5.5.1 Meta-RL
5.5.2 Goal-conditioned RL as meta-RL?
5.5.3 Curriculum learning as unsupervised meta-RL
5.5.4 Goal space identification
5.6 Observational and hierarchical RL: a global perspective
5.6.1 Hierarchical RL
5.6.2 Observational learning
6 Conclusion
