Plan4MC: Skill Reinforcement Learning and Planning for Open-World Minecraft Tasks

Plan4MC is a multi-task agent in Minecraft, solving long-term tasks via planning over basic skills. It acquire three types of fine-grained basic skills through reinforcement learning without demonstrations. With a skill graph pre-generated by the Large Language Model, the skill search algorithm generates skill plans and interactively selects policies to solve complicated tasks. Plan4MC accomplishes 24 diverse hard tasks in Minecraft.

Installation

• Install MineDojo environment following the official document. It requires python >= 3.9. We install jdk 1.8.0_171.

• Upgrade the MineDojo package:

  • Delete the original package pip uninstall minedojo.
  • Download our modified MineDojo. Run python setup.py install.
  • To this end, you can successfully run validate_install.py here.

• Install python packages in requirements.txt. Note that we require PyTorch>=1.8.1 and x-transformers==0.27.1.

• Download the pretrained MineCLIP model named attn.pth. Move the file to mineclip_official/.

Tasks

• We provide 24 diverse tasks configured in envs/hard_task_conf.yaml. To create an environment for a task:

from envs.minecraft_hard_task import MinecraftHardHarvestEnv
from minedojo.sim import InventoryItem
import utils
task_name = 'harvest_mutton_with_diamond_sword'
task_conf = utils.get_yaml_data('envs/hard_task_conf.yaml')[task_name]
init_items = {}
if 'initial_inventory' in task_conf:
    init_items = task_conf['initial_inventory']
    init_inv = [InventoryItem(slot=i, name=k, variant=None, quantity=task_conf['initial_inventory'][k]) for i,k in enumerate(list(task_conf['initial_inventory'].keys()))]
    task_conf['initial_inventory'] = init_inv
env = MinecraftHardHarvestEnv(image_size=(160,256), **task_conf)

• To generate task names, initial plans and involved skills for all the tasks, run python plan_all_tasks.py.

Plan4MC

• Pre-trained models for skills are released in skills/models/.

• To test Plan4MC on the task 'get_furnace_nearby_with_logs' as an example, run python test.py --task get_furnace_nearby_with_logs. Arguments:

  --task: available tasks are listed in envs/hard_task_conf.yaml.

  --save-path: output directory for testing results.

  --test-episode: number of testing episodes.

  --save-gif: set to 1 to save testing videos.

Citation

@article{yuan2023plan4mc,
      title={{Plan4MC}: Skill Reinforcement Learning and Planning for Open-World {Minecraft} Tasks}, 
      author={Yuan, Haoqi and Zhang, Chi and Wang, Hongcheng and Xie, Feiyang and Cai, Penglin and Dong, Hao and Lu, Zongqing},
      journal={arXiv preprint arXiv:2303.16563},
      year={2023},
}