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In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts.
Language models are few-shot learners
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Language models are few-shot learners
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Nachum, O., Gu, S. S., Lee, H., and Levine, S · 2018
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Nichol, A., Achiam, J., and Schulman, J · 2018
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Offline meta reinforcement learning–identifiability challenges and effective data collection strategies
Dorfman, R., Shenfeld, I., and Tamar, A · 2021
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A minimalist approach to offline reinforcement learning
Fujimoto, S. and Gu, S. S · 2021
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Offline reinforcement learning as one big sequence modeling problem
Janner, M., Li, Q., and Levine, S · 2021
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Mitchell, E., Rafailov, R., Peng, X. B., Levine, S., and Finn, C · 2021
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Off-policy deep reinforcement learning without exploration
Fujimoto, S., Meger, D., and Precup, D · 2019
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Conservative q-learning for offline reinforcement learning
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Flamingo: a visual language model for few-shot learning
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Multi-game decision transformers
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Offline meta-reinforcement learning with online self-supervision
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In-context reinforcement learning with algorithm distillation
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Chain of hindsight aligns language models with feedback
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