Abstract
Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by low sample efficiency, and mostly focus on low-dimensional task distributions. In parallel, model-based RL methods have been successful in solving partially observable MDPs, of which meta-RL is a special case. In this work, we leverage this success and propose a new model-based approach to meta-RL, based on elements from existing state-of-the-art model-based and meta-RL methods. We demonstrate the effectiveness of our approach on common meta-RL benchmark domains, attaining greater return with better sample efficiency (up to 15×) while requiring very little hyperparameter tuning. In addition, we validate our approach on a slate of more challenging, higher-dimensional domains, taking a step towards real-world generalizing agents.
Original language | English |
---|---|
State | Published - 2024 |
Event | 12th International Conference on Learning Representations, ICLR 2024 - Hybrid, Vienna, Austria Duration: 7 May 2024 → 11 May 2024 |
Conference
Conference | 12th International Conference on Learning Representations, ICLR 2024 |
---|---|
Country/Territory | Austria |
City | Hybrid, Vienna |
Period | 7/05/24 → 11/05/24 |
All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Computer Science Applications
- Education
- Linguistics and Language