Model-Agnostic Meta-Learning with Open-Ended Reinforcement Learning
Track: Tiny paper track
Keywords: Open-Ended, MAML, RL, Meta-Learning
Abstract: This paper is an in-progress research that builds on the Open-Ended Reinforcement Learning with Neural Reward Functions proposed by Meier and Mujika [1] which use reward functions encoded by neural networks. One key limitation of their paper is the necessity of re-learning for each new skill learned by the agent. Consequently, we propose integrating meta-learning algorithms to tackle this problem. We, therefore, study the use of MAML, Model-Agnostic Meta Learning that we believe could make policy learning more efficient. MAML operates by learning an initialization of the model parameters that can be fine-tuned with a small number of examples from a new task which allows for rapid adaptation to new tasks.
Submission Number: 36
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