Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
cs.AI
Submitted: 2026-05-23
Updated: 2026-09-24
Code: https://github.com/AHT-Hub/ICRL4AHT
Terminology
Sources
- Melting Pot 2.0
- RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
- Gaussian Error Linear Units (GELUs)
- Population Based Training of Neural Networks
- A Survey of In-Context Reinforcement Learning
- Proximal Policy Optimization Algorithms
- Yes, Q-learning Helps Offline In-Context RL
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