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Agent Q is a brand-new self-supervised agent reasoning and search framework that has been officially released after six months of development. This framework focuses on autonomous improvement in real-world tasks and internet environments through self-play and reinforcement learning (RL). It harnesses state-of-the-art large language models (LLMs) to process web content, create task plans, and engage in reasoning in natural language form, particularly suitable for tasks spanning long time frames.
Agent Q possesses advanced planning and self-healing capabilities. It combines cutting-edge technologies such as Monte Carlo Tree Search (MCTS), AI self-critique, and reinforcement learning from human feedback (RLFH), enabling AI to engage in complex multi-step reasoning and decision-making in dynamic environments.
Imagine yourself in a very large maze searching for the exit, with many forks in the path, each potentially leading you further away or closer to the exit. Agent Q is like a very intelligent assistant; it not only helps you analyze the possibilities of each path but also self-reflects when taking the wrong path to avoid the same mistake in the future.