Minecraft Planning Benchmark: Challenges for Long-Horizon Planning in Large Worlds

Core Concepts

The MinePlanner benchmark presents a set of challenging planning tasks in large Minecraft worlds, exposing the limitations of current domain-independent planners.

Abstract

The authors propose MinePlanner, a new benchmark for planning tasks based on the Minecraft game. The benchmark contains 45 tasks of varying difficulty, ranging from simple navigation to complex construction of structures like log cabins. The key highlights of the benchmark are: Minecraft provides a rich and open-ended environment for long-horizon planning tasks, with a large number of objects that may or may not be relevant to the task at hand. The benchmark automatically generates PDDL representations of the tasks, supporting both propositional and numeric planning. This allows for the evaluation of different planning approaches. The authors benchmark two state-of-the-art planners, Fast Downward and ENHSP-20, on the tasks. The results show that current planners struggle to scale to the large number of objects in the Minecraft worlds, with most tasks being unsolvable within a 2-hour time limit. The authors also investigate the use of task scoping to remove irrelevant objects, but find that this approach provides little benefit, as the grounding step itself becomes a bottleneck for the larger problem instances. Experiments with lifted planning using the Powerlifted system also indicate that the memory requirements of these tasks exceed the capabilities of existing planners. The authors conclude that the MinePlanner benchmark presents significant challenges for current planning approaches, and hope that it will spur the development of new techniques capable of operating in large, object-dense environments.

Stats

There are over 5000 objects in the "hard" variants of the tasks. The "easy" variants have between 762 and 876 initial predicates. The "medium" variants have between 1881 and 2400 initial predicates. The "hard" variants have between 18201 and 54663 initial predicates.

Quotes

"A major challenge in AI is the construction of autonomous agents capable of solving extremely long-horizon tasks." "If we hope to scale these approaches to real-world tasks and develop truly autonomous agents, then planners must be capable of operating in domains that contain a large number of objects that may or may not be relevant to the task at hand."

Key Insights Distilled From

MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds

by William Hill... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2312.12891.pdf

Deeper Inquiries

How can we develop planning techniques that can effectively reason about and prune irrelevant objects in large, complex environments like Minecraft?

To develop planning techniques that can effectively reason about and prune irrelevant objects in large, complex environments like Minecraft, we can consider the following strategies: Task Scoping Algorithms: Implement algorithms like task scoping, as introduced in previous research, to automatically generate task-specific abstractions for planning in open-scope models. These algorithms can help identify and remove irrelevant objects and operators from the planning domain, improving planner performance by focusing on the essential elements of the task. Lifted Planning: Explore lifted planning approaches that operate directly on the lifted representation of the problem. Lifted planning techniques can handle symbolic representations more efficiently, potentially reducing the need for extensive grounding and enabling planners to reason about high-level concepts without the need to consider every individual object. Predicate Invention: Investigate techniques like predicate invention, where planners can dynamically generate new predicates during planning to capture relevant information and abstract away unnecessary details. This adaptive approach can help planners adapt to the specific requirements of complex tasks and environments like those in Minecraft. Integration of Learning: Integrate machine learning and data-driven approaches to assist in identifying relevant objects and relationships in the environment. By leveraging learned representations and patterns, planners can focus on the most critical aspects of the task and avoid getting bogged down by irrelevant details. Hierarchical Planning: Explore hierarchical planning methods that can break down complex tasks into subtasks and levels of abstraction. By organizing the planning process hierarchically, planners can effectively manage the complexity of large environments and focus on relevant objects and actions at each level of the hierarchy.

How can the MinePlanner benchmark be extended or combined with other approaches, such as reinforcement learning, to create more comprehensive and challenging testbeds for developing truly autonomous agents?

The MinePlanner benchmark can be extended and combined with other approaches, such as reinforcement learning, to create more comprehensive and challenging testbeds for developing truly autonomous agents by: Incorporating Reinforcement Learning Environments: Integrate MinePlanner tasks into reinforcement learning environments like OpenAI Gym or DeepMind Lab to enable agents to interact with and learn from the Minecraft world. This integration allows for the training of agents using RL algorithms in the challenging and diverse scenarios provided by MinePlanner. Hybrid Planning-Reinforcement Learning Approaches: Develop hybrid approaches that combine classical planning techniques from MinePlanner with reinforcement learning methods. By leveraging the strengths of both paradigms, agents can benefit from the structured planning capabilities of MinePlanner tasks while also learning adaptive and flexible behaviors through RL. Multi-Task Learning: Extend the MinePlanner benchmark to include a diverse set of tasks that cover a wide range of planning challenges in Minecraft. By creating a multi-task learning environment, agents can learn generalizable skills and strategies that transfer across different scenarios, enhancing their overall performance and adaptability. Curriculum Learning: Design a curriculum of tasks within MinePlanner that progressively increase in complexity and difficulty. Agents can follow this curriculum to gradually build up their skills and knowledge, starting from simple tasks and advancing to more complex challenges, facilitating more effective learning and skill acquisition. Interactive Learning Environments: Create interactive learning environments within MinePlanner where agents can receive feedback and guidance during the planning process. By incorporating human feedback or teacher signals, agents can learn from demonstrations and corrections, accelerating their learning and improving their planning abilities. By extending and combining MinePlanner with these approaches, researchers can create a rich and diverse set of environments for training and evaluating autonomous agents, pushing the boundaries of AI capabilities in complex and dynamic worlds like Minecraft.