In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought

• URL: http://arxiv.org/abs/2405.20692v1
• Date: Fri, 31 May 2024 08:38:25 GMT
• Title: In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
• Authors: Sili Huang, Jifeng Hu, Hechang Chen, Lichao Sun, Bo Yang,
• Abstract summary: We propose an In-context Decision Transformer (IDT) to achieve self-improvement in a high-level trial-and-error manner. IDT is inspired by the efficient hierarchical structure of human decision-making. IDT achieves state-of-the-art in long-horizon tasks over current in-context RL methods.
• Score: 13.034968416139826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem. Despite the self-improvement not requiring gradient updates, current works still suffer from high computational costs when the across-episodic sequence increases with task horizons. To this end, we propose an In-context Decision Transformer (IDT) to achieve self-improvement in a high-level trial-and-error manner. Specifically, IDT is inspired by the efficient hierarchical structure of human decision-making and thus reconstructs the sequence to consist of high-level decisions instead of low-level actions that interact with environments. As one high-level decision can guide multi-step low-level actions, IDT naturally avoids excessively long sequences and solves online tasks more efficiently. Experimental results show that IDT achieves state-of-the-art in long-horizon tasks over current in-context RL methods. In particular, the online evaluation time of our IDT is \textbf{36$\times$} times faster than baselines in the D4RL benchmark and \textbf{27$\times$} times faster in the Grid World benchmark.

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