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Incremental learning of skill collections based on intrinsic motivation
1Robotics Research Group, Faculty 3 - Mathematics and Computer Science, Universität Bremen Bremen, Germany.
Frontiers in Neurorobotics
|July 31, 2013
Summary
This study introduces an intrinsically motivated, skill-learning approach for embodied agents. This method enables efficient, self-motivated skill acquisition during developmental periods without external rewards, outperforming agents learning from scratch.
Area of Science:
- Robotics
- Artificial Intelligence
- Machine Learning
Background:
- Embodied agents require lifelong learning of versatile skills for complex environments.
- Skill acquisition in agents often relies on imposed tasks and external rewards.
- Developmental periods offer a window for self-motivated, incremental skill learning.
Purpose of the Study:
- To propose a novel, incremental, task-independent skill discovery approach for continuous domains.
- To investigate the role of intrinsic motivation in guiding skill learning during agent development.
- To evaluate the efficiency of self-motivated skill learning compared to traditional methods.
Main Methods:
- Developed an incremental, task-independent skill discovery method for continuous domains.
- Utilized intrinsic motivation mechanisms to direct skill learning focus.
- Evaluated the approach in a reinforcement learning setup with complex dynamics.
Main Results:
- The intrinsically motivated skill-learning agent significantly outperformed agents learning task solutions from scratch.
- Different intrinsic motivation mechanisms were compared for their efficiency in utilizing the developmental period.
- The proposed approach demonstrated effective skill discovery in complex, continuous environments.
Conclusions:
- Intrinsically motivated, self-directed skill learning is an efficient paradigm for embodied agents.
- Task-independent skill discovery during developmental periods is crucial for lifelong learning.
- Intrinsic motivation effectively guides agents to acquire versatile skills autonomously.
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