%global _empty_manifest_terminate_build 0
Name: python-minerl
Version: 0.4.4
Release: 1
Summary: MineRL environment and data loader for reinforcement learning from human demonstration in Minecraft
License: MIT
URL: http://github.com/minerllabs/minerl
Source0: https://mirrors.aliyun.com/pypi/web/packages/08/c1/651340d34dc6a165821c8aba61903db7c5ac874827efb185e78e35086835/minerl-0.4.4.tar.gz
BuildArch: noarch
%description
# The [MineRL](http://minerl.io) Python Package
[](https://minerl.readthedocs.io/en/latest/?badge=latest)
[](https://buildkite.com/openai-mono/minerl-public-dev)
[](https://pepy.tech/project/minerl)
[](https://badge.fury.io/py/minerl)
[](https://github.com/minerllabs/minerl/issues)
[](https://github.com/minerllabs/minerl/issues?utf8=%E2%9C%93&q=is%3Aissue+is%3Aopen+label%3Abug)
[](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/)!**
## Installation
With JDK-8 installed run this command
```
pip3 install --upgrade minerl
```
## Basic Usage
Running an environment:
```python
import minerl
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
# YOU ONLY NEED TO DO THIS ONCE!
minerl.data.download('/your/local/path')
data = minerl.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.batch_iter(
num_epochs=1, seq_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:
```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.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-2021-minerl-competition).
%package -n python3-minerl
Summary: MineRL environment and data loader for reinforcement learning from human demonstration in Minecraft
Provides: python-minerl
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-minerl
# The [MineRL](http://minerl.io) Python Package
[](https://minerl.readthedocs.io/en/latest/?badge=latest)
[](https://buildkite.com/openai-mono/minerl-public-dev)
[](https://pepy.tech/project/minerl)
[](https://badge.fury.io/py/minerl)
[](https://github.com/minerllabs/minerl/issues)
[](https://github.com/minerllabs/minerl/issues?utf8=%E2%9C%93&q=is%3Aissue+is%3Aopen+label%3Abug)
[](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/)!**
## Installation
With JDK-8 installed run this command
```
pip3 install --upgrade minerl
```
## Basic Usage
Running an environment:
```python
import minerl
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
# YOU ONLY NEED TO DO THIS ONCE!
minerl.data.download('/your/local/path')
data = minerl.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.batch_iter(
num_epochs=1, seq_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:
```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.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-2021-minerl-competition).
%package help
Summary: Development documents and examples for minerl
Provides: python3-minerl-doc
%description help
# The [MineRL](http://minerl.io) Python Package
[](https://minerl.readthedocs.io/en/latest/?badge=latest)
[](https://buildkite.com/openai-mono/minerl-public-dev)
[](https://pepy.tech/project/minerl)
[](https://badge.fury.io/py/minerl)
[](https://github.com/minerllabs/minerl/issues)
[](https://github.com/minerllabs/minerl/issues?utf8=%E2%9C%93&q=is%3Aissue+is%3Aopen+label%3Abug)
[](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/)!**
## Installation
With JDK-8 installed run this command
```
pip3 install --upgrade minerl
```
## Basic Usage
Running an environment:
```python
import minerl
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
# YOU ONLY NEED TO DO THIS ONCE!
minerl.data.download('/your/local/path')
data = minerl.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.batch_iter(
num_epochs=1, seq_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:
```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.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-2021-minerl-competition).
%prep
%autosetup -n minerl-0.4.4
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-minerl -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.4-1
- Package Spec generated