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Found 2 papers, 1 papers with code
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
Developing such algorithms requires tasks that can gauge progress on algorithm design, provide accessible and reproducible evaluations for multi-turn interactions, and cover a range of task properties and challenges in improving reinforcement learning algorithms.
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation
Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in an autonomous way without human intervention, enabling continual learning under realistic assumptions.
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