Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

Autor:	Zintgraf, L, Feng, L, Lu, C, Igl, M, Hartikainen, K, Hofmann, K, Whiteson, S
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Statistics - Machine Learning Machine Learning (stat.ML) Machine Learning (cs.LG)
Popis:	To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep. One way to learn such behaviour is via meta-learning. Many existing methods however rely on dense rewards for meta-training, and can fail catastrophically if the rewards are sparse. Without a suitable reward signal, the need for exploration during meta-training is exacerbated. To address this, we propose HyperX, which uses novel reward bonuses for meta-training to explore in approximate hyper-state space (where hyper-states represent the environment state and the agent's task belief). We show empirically that HyperX meta-learns better task-exploration and adapts more successfully to new tasks than existing methods. Published at the International Conference on Machine Learning (ICML) 2021
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7efde79bd27f9436f4548bcea2be318a http://arxiv.org/abs/2010.01062 Zobrazit plný text záznamu