Minecraft agent 2023-05-26
Voyager minedojo icon

Voyager minedojo

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Minecraft lifelong learning agent.
Generated by ChatGPT

Voyager is an open-ended embodied agent with large language models that operates in the Minecraft environment. It is the first agent of its kind to achieve lifelong learning without human intervention.

Voyager consists of three main components: an automatic curriculum, a skill library, and an iterative prompting mechanism. The automatic curriculum maximizes exploration by considering the agent's progress and state.

It generates tasks based on the goal of discovering diverse things, similar to an in-context novelty search. The skill library stores and retrieves complex behaviors.

Each skill is indexed by the embedding of its description, allowing for easy retrieval in similar situations. Complex skills can be synthesized by composing simpler programs, enhancing Voyager's capabilities over time and preventing catastrophic forgetting.

The iterative prompting mechanism incorporates environment feedback, execution errors, and self-verification for program improvement. It allows Voyager to learn from its mistakes and refine its skills.

Voyager interacts with GPT-4, a large language model, through blackbox queries, eliminating the need for fine-tuning of model parameters. In experiments, Voyager demonstrates strong lifelong learning capabilities and exceptional proficiency in playing Minecraft.

It outperforms previous state-of-the-art approaches by obtaining more unique items, covering longer distances, and achieving key milestones in the tech tree at a faster rate.

It also outperforms other techniques in generalizing to novel tasks in new Minecraft worlds. Voyager continually discovers new items and skills through self-driven exploration, surpassing baseline methods.

Overall, Voyager combines the power of large language models with embodied learning to create an agent capable of continuous exploration, skill development, and novel discoveries in open-ended environments like Minecraft.


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Voyager minedojo was manually vetted by our editorial team and was first featured on June 28th 2023.
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Pros and Cons


Agent operates in Minecraft
Achieves lifelong without human intervention
Consists of automatic curriculum
Skill Library
Iterative prompting mechanism
Maximizes exploration
Generates tasks based on progress
Stores and retrieves complex behaviors
Indexes skills for quick retrieval
Synthesizes complex skills in composition
Prevents catastrophic forgetting
Incorporates environment feedback
Learns from execution errors
Refines skills via self-verification
Interacts with GPT-4 via Blackbox
No requirement for fine-tuning
Shows lifelong learning capability
Proficient in playing Minecraft
Outperforms SOTA approaches
Discovers new items
Achieves Minecraft milestones faster
Optimizes map coverage
Capable of generalizing to novel tasks
Continual self-driven exploration
Leverages large Language Models
Capable of continuous skill development
Functions in open-ended environments
Provides in-context novelty search
Enhances capabilities over time
Automatically proposes suitable tasks
Refines skills based on feedback
Memory feature for mastered skills
Code used as action space
Bypasses requirement for gradient-based training
Can apply learned skills to unseen tasks
Showcases extensive terrain traversal skill
Enhances other methods via skill library
Achieves higher map coverage than competitors
Efficient in solving zero-shot tasks
Outperforms GPT-3.5 for code generation
Successfully unlocked Minecraft's tech tree
Achieves zero-shot generalization to unseen tasks
Boosts AutoGPT performance via Skill Library
Leverages GPT-4 to continuously explore Minecraft
Develops sophisticated skills overtime
Makes new discoveries consistently without human intervention
Exhibits outstanding performance in various tasks
Does not require model parameters tuning
Develops websit ite through borrowing templates
Ablation studies confirming the importance of each component
Provides website template for future projects


Only works in Minecraft
Overly complex architecture
Dependent on GPT-4
Interaction method not specified
Requires storage for skill library
Interactions susceptible to GPT-4 limitations
Curriculum generation lacks clarity
No detail on error handling
Potential for catastrophic forgetting
Skill indexing and retrieval system vague


What is Voyager?
How does Voyager interact with the Minecraft environment?
What are the main components of Voyager?
How does the automatic curriculum of Voyager work?
What is the role of the skill library in Voyager?
Can you tell me more about Voyager's iterative prompting mechanism?
How does Voyager interact with GPT-4?
What capabilities does Voyager have?
How well did Voyager perform in your experiments?
Can Voyager perform tasks in new Minecraft worlds?
How does Voyager augment its skill set?
What is the significance of Voyager's interaction with GPT-4?
What makes Voyager different from other AI agents?
Can Voyager adapt to different tasks and environments?
How does Voyager prevent catastrophic forgetting?
How complex are the programs that Voyager can compose?
What allows Voyager to explore the Minecraft environment so efficiently?
Is Voyager capable of zero-shot generalization to unseen tasks?
Does Voyager require any human intervention?
How does Voyager make use of large language models?


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