Summary of Worldcoder, a Model-based Llm Agent: Building World Models by Writing Code and Interacting with the Environment, By Hao Tang et al.
WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
by Hao Tang, Darren Key, Kevin Ellis
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to building a model-based agent that constructs a Python program representing its understanding of the world based on interactions with the environment. The agent’s world model aims to explain its experiences while also considering potential rewards. A key aspect is the introduction of optimism as a logical constraint between the program and planner. The authors evaluate their method on gridworlds and task planning, demonstrating that it outperforms deep RL in terms of sample efficiency, ReAct-style agents in terms of compute efficiency, and can transfer knowledge across environments by editing its code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of computer agent that builds a program to understand the world based on how it interacts with things. The program tries to make sense of what’s happening while also thinking about what rewards it might get. A key part is making sure the program is optimistic, or hopeful, about what it can achieve. Researchers tested this approach in different scenarios and found that it was more efficient than other methods in some cases, and could even learn from one environment to another by editing its code. |
