Metadata-Version: 2.1
Name: minerl-patched
Version: 0.1.0
Summary: MineRL environment and data loader for reinforcement learning from human demonstration in Minecraft
Home-page: https://github.com/iglu-contest/minerl
Author: MineRL Labs
Author-email: minerl@andrew.cmu.edu
License: MIT
Description: # IGLU patch
        
        This repo contains Minerl package with patched Malmo with custom blocks and behaviour added. 
        
        # The [MineRL](http://minerl.io) Python Package
        
        [![Build Status](https://travis-ci.com/minerllabs/minerl.svg?branch=master)](https://travis-ci.com/minerllabs/minerl)
        [![Dev Build status](https://badge.buildkite.com/0717cd35b9a708f0b4ac0b2858eec9ca7d08f6768868d3ac08.svg)](https://buildkite.com/openai-mono/minerl-public-dev)
        [![Downloads](https://pepy.tech/badge/minerl)](https://pepy.tech/project/minerl)
        [![PyPI version](https://badge.fury.io/py/minerl.svg)](https://badge.fury.io/py/minerl)
        [!["Open Issues"](https://img.shields.io/github/issues-raw/minerllabs/minerl.svg)](https://github.com/minerllabs/minerl/issues)
        [![GitHub issues by-label](https://img.shields.io/github/issues/minerllabs/minerl/bug.svg?color=red)](https://github.com/minerllabs/minerl/issues?utf8=%E2%9C%93&q=is%3Aissue+is%3Aopen+label%3Abug)
        [![Discord](https://img.shields.io/discord/565639094860775436.svg?label=&logo=discord&logoColor=ffffff&color=7389D8&labelColor=6A7EC2)](https://discord.gg/BT9uegr)
        
        
        Python package providing easy to use gym environments and a simple data api for the MineRLv0 dataset. 
        
        **To [get started please read the docs here](http://minerl.io/docs/)!**
        
        ![](http://www.minerl.io/docs/_images/demo.gif)
        ## Installation
        
        With JDK-8 installed run this command
        ```
        pip3 install --upgrade minerl
        ```
        
        ## Basic Usage
        
        Running an environment:
        ```python
        import minerl_patched
        import gym
        env = gym.make('MineRLNavigateDense-v0')
        
        
        obs = env.reset()
        
        done = False
        while not done:
            action = env.action_space.sample() 
         
            # One can also take a no_op action with
            # action =env.action_space.noop()
            
         
            obs, reward, done, info = env.step(
                action)
        
        ```
        
        Sampling the dataset:
        
        ```python
        import minerl_patched
        
        # YOU ONLY NEED TO DO THIS ONCE!
        minerl_patched.data.download('/your/local/path')
        
        data = minerl_patched.data.make(
            'MineRLObtainDiamond-v0',
            data_dir='/your/local/path')
        
        # Iterate through a single epoch gathering sequences of at most 32 steps
        for current_state, action, reward, next_state, done \
            in data.sarsd_iter(
                num_epochs=1, max_sequence_len=32):
        
                # Print the POV @ the first step of the sequence
                print(current_state['pov'][0])
        
                # Print the final reward pf the sequence!
                print(reward[-1])
        
                # Check if final (next_state) is terminal.
                print(done[-1])
        
                # ... do something with the data.
                print("At the end of trajectories the length"
                      "can be < max_sequence_len", len(reward))
        ```
        
        
        Visualizing the dataset:
        
        ![viewer|540x272](http://www.minerl.io/docs/_images/cropped_viewer.gif)
        ```bash
        
        # Make sure your MINERL_DATA_ROOT is set!
        export MINERL_DATA_ROOT='/your/local/path'
        
        # Visualizes a random trajectory of MineRLObtainDiamondDense-v0
        python3 -m minerl_patched.viewer MineRLObtainDiamondDense-v0
        
        ```
        
        ## MineRL Competition
        If you're here for the MineRL competition. Please check [the main competition website here](https://www.aicrowd.com/challenges/neurips-2020-minerl-competition).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
