AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers

• URL: http://arxiv.org/abs/2411.11188v1
• Date: Sun, 17 Nov 2024 22:25:40 GMT
• Title: AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
• Authors: Jake Grigsby, Justin Sasek, Samyak Parajuli, Daniel Adebi, Amy Zhang, Yuke Zhu,
• Abstract summary: We build upon recent advancements in Transformer-based (in-context) meta-RL. We evaluate a simple yet scalable solution where both an agent's actor and critic objectives are converted to classification terms. This design unlocks significant progress in online multi-task adaptation and memory problems without explicit task labels.
• Score: 28.927809804613215
• License:
• Abstract: Language models trained on diverse datasets unlock generalization by in-context learning. Reinforcement Learning (RL) policies can achieve a similar effect by meta-learning within the memory of a sequence model. However, meta-RL research primarily focuses on adapting to minor variations of a single task. It is difficult to scale towards more general behavior without confronting challenges in multi-task optimization, and few solutions are compatible with meta-RL's goal of learning from large training sets of unlabeled tasks. To address this challenge, we revisit the idea that multi-task RL is bottlenecked by imbalanced training losses created by uneven return scales across different tasks. We build upon recent advancements in Transformer-based (in-context) meta-RL and evaluate a simple yet scalable solution where both an agent's actor and critic objectives are converted to classification terms that decouple optimization from the current scale of returns. Large-scale comparisons in Meta-World ML45, Multi-Game Procgen, Multi-Task POPGym, Multi-Game Atari, and BabyAI find that this design unlocks significant progress in online multi-task adaptation and memory problems without explicit task labels.

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