MineRL: A Large-Scale Dataset of Minecraft Demonstrations (1907.13440v1)

Abstract: The sample inefficiency of standard deep reinforcement learning methods precludes their application to many real-world problems. Methods which leverage human demonstrations require fewer samples but have been researched less. As demonstrated in the computer vision and natural language processing communities, large-scale datasets have the capacity to facilitate research by serving as an experimental and benchmarking platform for new methods. However, existing datasets compatible with reinforcement learning simulators do not have sufficient scale, structure, and quality to enable the further development and evaluation of methods focused on using human examples. Therefore, we introduce a comprehensive, large-scale, simulator-paired dataset of human demonstrations: MineRL. The dataset consists of over 60 million automatically annotated state-action pairs across a variety of related tasks in Minecraft, a dynamic, 3D, open-world environment. We present a novel data collection scheme which allows for the ongoing introduction of new tasks and the gathering of complete state information suitable for a variety of methods. We demonstrate the hierarchality, diversity, and scale of the MineRL dataset. Further, we show the difficulty of the Minecraft domain along with the potential of MineRL in developing techniques to solve key research challenges within it.

• The paper introduces MineRL, a dataset addressing sample inefficiency in deep reinforcement learning with over 60 million annotated state-action pairs.
• It employs an innovative data collection framework via a public Minecraft server to continuously expand and diversify task data.
• Empirical evaluations reveal a significant gap between human and RL agent performance, emphasizing the need for hierarchical learning improvements.

An Analysis of "MineRL: A Large-Scale Dataset of Minecraft Demonstrations"

The paper "MineRL: A Large-Scale Dataset of Minecraft Demonstrations" presents a significant contribution to the reinforcement learning (RL) community, particularly in the context of leveraging human demonstrations to address the inherent sample inefficiency in standard deep reinforcement learning methods. The authors introduce MineRL, a comprehensive dataset composed of over 60 million automatically annotated state-action pairs from the popular game Minecraft. This dataset is coupled with a data collection framework facilitating the ongoing acquisition of new tasks and improved data breadth, enabling the development of more sample-efficient RL methods.

Overview and Contributions

The paper's key contributions can be summarized as follows:

1. Dataset Scope and Content: The MineRL dataset is meticulously crafted to include a wide range of tasks within Minecraft, capturing the complexity of real-world challenges. It includes six distinct tasks and a substantial volume of human gameplay data amounting to over 500 hours. Each task offers unique challenges such as multi-agent interactions, long-term planning, and hierarchical task completion characteristic of open-world environments like Minecraft.
2. Innovative Data Collection: The authors propose a novel data collection method through a public game server, which enables the dynamic accumulation of state-action pairs. This approach not only supports the sustained growth of the dataset but also allows seamless integration of newly crafted tasks as the game evolves.
3. Hierarchical and Diverse Nature: The dataset's hierarchality is carefully demonstrated, encapsulating both the intricate sub-task dependencies and the varied strategies employed by human players to overcome these challenges. This makes it an ideal benchmark for evaluating hierarchical reinforcement learning methods.
4. Task-Specific Insights: The paper outlines detailed task configurations such as Navigation, Tree Chopping, and Item Acquisition, each serving as a microcosm of the larger Minecraft environment. These tasks cover a spectrum of gameplay elements from purely navigational and resource-gathering exercises to complex survival challenges, thereby providing a robust evaluation framework for RL algorithms.
5. Empirical Evaluations: To highlight the dataset's utility, multiple RL and imitation learning methods, including DQN and behavioral cloning, are evaluated with MineRL. The results underscore the substantial gap between human-level performance and current RL methodologies, pointing towards the pressing need for improved learning strategies.

Implications and Future Directions

The introduction of MineRL holds significant implications for both theoretical and practical dimensions of RL research:

• Theoretical Insights: The diversity and complexity encoded in MineRL enable exploration into various RL paradigms—such as imitation learning, hierarchical learning, and transfer learning—potentially guiding advancements in these areas.
• Practical Impact: As RL increasingly ventures into real-world applications, datasets such as MineRL that closely mimic the challenges of open-world environments can bridge the gap between controlled simulations and unpredictable real-life scenarios.
• Foundation for Future Work: The paper lays the groundwork for future explorations aimed at addressing the deficiencies in current RL techniques. For instance, the integration of MineRL with augmented reality interfaces or more sophisticated human-RL agent collaborations could be potential avenues for exploration.

In conclusion, "MineRL: A Large-Scale Dataset of Minecraft Demonstrations" offers a substantial leap forward in the domain of RL research by providing a well-structured, large-scale dataset capable of facilitating nuanced explorations into advanced learning paradigms. Its potential to foster methodological innovations and practical applications is significant, making it a cornerstone resource for AI researchers focused on improving RL efficacy in complex, dynamic environments.