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Designing reward functions is a longstanding challenge in reinforcement learning (RL); it requires specialized knowledge or domain data, leading to high costs for development.
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Proximal policy optimization algorithms
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Virtualhome: Simulating household activities via programs
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
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Evaluating large language models trained on code
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Training verifiers to solve math word problems
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Silg: The multi-domain symbolic interactive language grounding benchmark
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Do as i can, not as i say: Grounding language in robotic affordances
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
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Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo
Reasoning with language model is planning with world model
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Few-shot preference learning for human-in-the-loop rl
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Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi · 2022
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Code as policies: Language model programs for embodied control
Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng · 2022
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R3m: A universal visual representation for robot manipulation
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Surf: Semi-supervised reward learning with data augmentation for feedback-efficient preference-based reinforcement learning, 2022
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Natural language to code translation with execution
Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I Wang · 2022
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su · 2022
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One embedder, any task: Instruction-finetuned text embeddings
Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, and Tao Yu · 2022
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Llama 2: Open foundation and fine-tuned chat models
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Language to rewards for robotic skill synthesis
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Webarena: A realistic web environment for building autonomous agents
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