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Fix broken link? (#214)

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Jacob Lee
2024-03-18 22:12:29 -07:00
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README.md
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- [Basic Reflection](./examples/reflection/reflection.ipynb): add a simple "reflect" step in your graph to prompt your system to revise its outputs.
- [Reflexion](./examples/reflexion/reflexion.ipynb): critique missing and superflous aspects of the agent's response to guide subsequent steps. Based on [Reflexion](https://arxiv.org/abs/2303.11366), by Shinn, et. al.
- [Language Agent Tree Search](./examples/lats/lats.ipynb): execute multiple agents in parallel, using reflection and environmental rewards to drive a Monte Carlo Tree Search. Based on [LATS](https://arxiv.org/abs/2310.04406/LanguageAgentTreeSearch/), by Zhou, et. al.
- [Language Agent Tree Search](./examples/lats/lats.ipynb): execute multiple agents in parallel, using reflection and environmental rewards to drive a Monte Carlo Tree Search. Based on [LATS](https://arxiv.org/abs/2310.04406), by Zhou, et. al.
### Multi-agent Examples
examples/lats/lats.ipynb
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"source": [
"# Language Agent Tree Search\n",
"\n",
"[Language Agent Tree Search](https://andyz245.github.io/LanguageAgentTreeSearch/) (LATS), by Zhou, et. al, is a general LLM agent search algorithm that combines reflection/evaluation and search (specifically monte-carlo trees search) to get achieve better overall task performance compared to similar techniques like ReACT, Reflexion, or Tree of Thoughts.\n",
"[Language Agent Tree Search](https://arxiv.org/abs/2310.04406) (LATS), by Zhou, et. al, is a general LLM agent search algorithm that combines reflection/evaluation and search (specifically monte-carlo trees search) to get achieve better overall task performance compared to similar techniques like ReACT, Reflexion, or Tree of Thoughts.\n",
"\n",
"![LATS diagram](./img/lats.png)\n",
"\n",