Tool Information
Voyager is an open-ended embodied agent powered by Large Language Models (LLMs) that continuously explores, acquires new abilities, and makes novel discoveries in the Minecraft environment without human intervention. The Voyager system is primarily made up of three components. First, an 'automatic curriculum' guides the system's exploration. This curriculum is determined based on the system's progress and state, with an overarching goal of discovering a diverse array of objects and features. Second, a 'skill library' stores and retrieves complex behaviors. Each acquired skill is indexed by the embedding of its description, which is later used to retrieve that skill when faced with similar situations, and the development of such skills is also vital in minimizing catastrophic forgetting. Third, an 'iterative prompting mechanism' generates executable code for control using the environment's feedback, execution errors, and self verification. Voyager interacts primarily through blackbox queries with a Large Language Model (LLMs). For its action space, the system uses code rather than low-level motor commands since the former can easily represent temporally extended actions and compositional tasks, which are necessary for numerous long-term tasks in Minecraft. Generally, Voyager can establish unique tasks based on its current skill level and state of the world, improve skills based on environmental feedback, commit skills to memory for future similar tasks and explore the world in a self-sufficient way, continually seeking new tasks to complete.
F.A.Q (20)
Voyager is an open-ended embodied agent that operates in the Minecraft environment using Large Language Models (LLMs). It is designed to continuously explore, acquire new abilities, and make novel discoveries without any human intervention.
Distinctly, Voyager is the first agent to achieve lifelong learning within an open-ended environment like Minecraft. It relies on its unique automatic curriculum to guide its exploration, stores and retrieves complex behaviors from its skill library, and utilizes an iterative prompting mechanism to generate executable code for control using environment feedback. Unlike others, Voyager interacts with GPT-4 via blackbox queries, and doesn't require fine-tuning of model parameters.
Voyager comprises of three components: an automatic curriculum, a skill library, and an iterative prompting mechanism. The automatic curriculum guides the system's self-driven exploration, the skill library stores and retrieves complex behaviors for future use, and the iterative prompting mechanism generates executable code using environment feedback and self-verification.
The automatic curriculum in Voyager maximizes exploration by considering the agent's progress and state. It generates tasks with the overarching goal of discovering diverse objects and features. This approach is similar to an in-context novelty search.
The skill library in Voyager serves the purpose of storing and retrieving complex behaviors. Each skill is indexed by the embedding of its description which can be easily retrieved in similar situations in the future. The skill library helps Voyager to develop progressively complex skills while also preventing catastrophic forgetting.
The iterative prompting mechanism in Voyager generates executable code that is used for control, taking into account elements such as environment feedback, execution errors, and self-verification for program improvement. This mechanism allows Voyager to learn from its mistakes and refine its skills.
Voyager interacts with GPT-4 through blackbox queries. This method of interaction eliminates the need for fine-tuning of model parameters.
Blackbox queries in Voyager act as a medium for the interaction between the agent and the Large Language Model (GPT-4). This approach eliminates the need for model parameter fine-tuning.
In the Minecraft environment, Voyager continuously explores, discovers new objects and features, stores complex behaviors, refines its skills based on environment feedback, avoids making the same mistakes, and performs a wide range of tasks. Voyager consistently proposes new tasks based on its current skill level and state of the world.
Compared to previous state-of-the-art methods, Voyager performs significantly better in Minecraft. It is able to discover more unique items, cover longer distances, and achieve key milestones in the tech tree at a faster rate. In comparison to other methods, Voyager also excels at generalizing to novel tasks within new Minecraft worlds.
Yes, Voyager has been shown to apply its learned skill library to solve novel tasks from scratch in new Minecraft worlds, demonstrating efficient zero-shot generalization capabilities.
To prevent catastrophic forgetting, Voyager builds an ever-growing skill library. Each new skill is indexed by its description's embedding, allowing Voyager to easily retrieve and utilize these skills in similar situations in the future. Moreover, complex skills in Voyager can be synthesized by composing simpler programs which contributes to the prevention of catastrophic forgetting.
Voyager refines its skills using a unique iterative prompting mechanism which incorporates environment feedback, execution errors, and self-verification. This mechanism allows Voyager to learn from its mistakes and improve.
When Voyager needs to retrieve a skill, it refers to the skill library. Each skill stored in the library is indexed by the embedding of its description. During retrieval, Voyager performs a query to identify the most relevant skills for a given task.
Voyager interacts with Large Language Models (LLMs) through blackbox queries. This approach, requiring no model parameter access or explicit gradient-based training, involves prompting and in-context learning with the LLM, effectively bypassing the need for fine-tuning.
Code-based action space in Voyager is opted over low-level motor commands as it efficiently represents temporally extended actions and compositional tasks that are essential for numerous long-term tasks in Minecraft.
Voyager can propose unique tasks based on its existing skill level and the state of the world using the automatic curriculum. This mechanism constantly guides the system's exploration aiming to discover as many unique and diverse objects as possible.
Voyager is capable of executing numerous long-term tasks in Minecraft, thanks to its code-based action space. This is essential for tasks requiring systematic and temporally extended actions, like crafting and using tools to progress through the tech tree.
Voyager's capability of continual exploration entails self-driven exploration of the Minecraft world, seeking out new tasks, refining skills based on environmental feedback, and progressing its abilities autonomously over time.
By continuously exploring, refining skills, and developing increasingly sophisticated behaviors, Voyager can make novel discoveries in open-ended environments like Minecraft. It's capable of achieving key milestones, covering longer distances, and discovering new items within these environments.
Pros and Cons
Pros
- Operates in Minecraft environment
- Achieves lifelong learning
- Does not require human intervention
- Automatic curriculum generation
- Maximizes exploration
- Skill library included
- Complex behaviors saved and indexed
- Iterative prompting mechanism
- Self-learning from mistakes
- Interacts with GPT-4
- No parameter fine-tuning needed
- Outperforms previous tools
- Obtains more unique items
- Covering longer distances
- Faster progress in tech tree
- Superior generalization to novel tasks
- Self-driven exploration
- Code-Based action space
- Temporal extension of skills
- Discovery of new items and skills
- In-context novelty search
- Catastrophic forgetting prevention
- Traverses variety of terrains
- Zero-shot generalization capability
- Effective code generation
- Consistent performance in task solving
- Efficient tech tree unlocking
- Efficient map traversal
Cons
- Limited to Minecraft environment
- Dependent on GPT-4
- Lack of model parameter fine-tuning
- Reliant on complex prompting mechanism
- Need for extensive skill library
- Complexity phasing low and high-level tasks
- Dependent on automatic curriculum for tasks
- Blackbox interaction limits transparency
- Potential catastrophic forgetting issue
- Probable inefficiency in random environments
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