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Overview

Minecraft, a globally popular sandbox video game, provides a dynamic, block-based universe where players build, explore, and interact with a variety of entities and landscapes. The project intends to train a tree chopping agent in the Minecraft environment using Deep Q-leanring from demonstration.

Requirements

The code depends on the following libraries:

  • Python 3.7
  • PyTorch 1.8.1
  • CUDA 11.2
  • MineRL
  • PFRL

The envionment of Minecraft is wrapped by MineRL, so please follow the document to install the MineRL library first. Be sure you could successfully run the following test code from the document :

import minerl
import gym
env = gym.make('MineRLNavigateDense-v0')


obs  = env.reset()
done = False
net_reward = 0

while not done:
    action = env.action_space.noop()

    action['camera'] = [0, 0.03*obs["compassAngle"]]
    action['back'] = 0
    action['forward'] = 1
    action['jump'] = 1
    action['attack'] = 1

    obs, reward, done, info = env.step(
        action)

    net_reward += reward
    print("Total reward: ", net_reward)

PFRL in a library implementing some state-of-art deep reinforcement learning algorithm. Our project use it for the prioritized buffer. You could find more details about their work here

Dataset

To download the demonstration data from human, you could follow the guidance in the document to the local path <YOUR LOCAL REPO PATH>/data/rawdata, or more specific:

sudo gedit ~/.bashrc

Add export MINERL_DATA_ROOT=<YOUR LOCAL REPO PATH>/data/rawdata at the end of the file and save it, then:

source ~./bashrc

Then download the specific dataset MineRLTreechopVectorObf-v0

python3 -m minerl.data.download "MineRLTreechopVectorObf-v0"

Then we should preprocess the dataset to extract the frames and calculate the actionspace:

python3 -u preprocess.py \
        --ROOT "./" \
        --DATASET_LOC "./data/rawdata/MineRLTreechopVectorObf-v0" \
        --actionNum 32 \
        --PREPARE_DATASET True \
        --n 25 \

It would generate the output frames in <YOUR LOCAL REPO PATH>/data/processdata and actionspace in <YOUR LOCAL REPO PATH>/actionspace

Train

If you like to train your own agent, be sure that your PC have at least 32 GB RAM:

python3 -u train.py \
        --ROOT "./" \
        --DATASET_LOC "./data/rawdata/MineRLTreechopVectorObf-v0" \
        --MODEL_SAVE "./saved_network"\
        --actionNum 32 \
        --n 25 \

Evaluate

We also provide our best train agent in <YOUR LOCAL REPO PATH>/saved_network/best_model.pt, you could run it by:

python3 -u evaluate.py \
        --ROOT "./" \
        --DATASET_LOC "./data/rawdata/MineRLTreechopVectorObf-v0" \
        --MODEL_SAVE "./saved_network"\
        --agentname best_model.pt
        --actionNum 32 \
        --n 25 \

The results of different architecture is shown in the table: