CraftGround: A Flexible Reinforcement Learning Environment Based on the Latest Minecraft

Hyeonseo Yang, Minsu Lee, Byoung-Tak Zhang

http://doi.org/10.5626/JOK.2025.52.3.189

This paper presents CraftGround, an innovative reinforcement learning environment based on the latest version of Minecraft (1.21). CraftGround provides flexible experimental setups and supports reinforcement learning in complex 3D environments, offering a variety of observational data, including visual information, audio cues, biome-specific contexts, and in-game statistics. Our experiments evaluated several agents, such as VPT (Video PreTraining), PPO, RecurrentPPO, and DQN, across various tasks, including tree chopping, evading hostile monsters, and fishing. The results indicated that VPT performed exceptionally well due to its pretraining, achieving higher performance and efficiency in structured tasks. In contrast, online learning algorithms like PPO and RecurrentPPO demonstrated a greater ability to adapt to environmental changes, showing improvement over time. These findings highlight CraftGround's potential to advance research on adaptive agent behaviors in dynamic 3D simulations.