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

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

17 Nov 2024arXiv:2411.11188archive 2025-07-28

Jake Grigsby, Justin Sasek, Samyak Parajuli, Daniel Adebi, Amy Zhang, Yuke Zhu

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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register_agent ut-austin-rpl/amago/amago/agent.py official repository ran · our draft was wrong MIT (permissive) · cb28542e368fc51b · report
RLData_pad_collate ut-austin-rpl/amago/amago/loading.py official repository unverified MIT (permissive) · 9069125b4b210054 · report
get_action_dim_and_type ut-austin-rpl/amago/amago/agent.py official repository unverified MIT (permissive) · 2ffee777eb103fb3 · report
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get_path_to_trajs ut-austin-rpl/amago/amago/loading.py official repository unverified MIT (permissive) · 7176a351fc668593 · report
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register_traj_encoder ut-austin-rpl/amago/amago/nets/traj_encoders.py official repository unverified MIT (permissive) · 60a00fe723fb7874 · report
softplus_bounded_positive ut-austin-rpl/amago/amago/nets/policy_dists.py official repository unverified MIT (permissive) · 9f588318c9339dd5 · report
tanh_bounded_positive ut-austin-rpl/amago/amago/nets/policy_dists.py official repository unverified MIT (permissive) · 679514cf7e0c892c · report
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Tasks

In-Context LearningMeta Reinforcement LearningMeta-LearningReinforcement Learning (RL)

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