1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
|
%global _empty_manifest_terminate_build 0
Name: python-hrl-pybullet-envs
Version: 0.2.37
Release: 1
Summary: Locomotion HRL envs in pybullet
License: GNU General Public License v3 or later (GPLv3+)
URL: https://github.com/sash-a/hrl_pybullet_envs
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4d/f3/626d039f4e3e48f37e8215fb17976364a103b9a27ad0c3c1b1c841364d85/hrl_pybullet_envs-0.2.37.tar.gz
BuildArch: noarch
%description
## Hierarchical Reinforcement envs in pybullet
This package was created because all of the HRL locomotion envs are only available in mujoco. This is an implementation of as many as possible in pybullet.
### Install
`pip install pybullet hrl_pybullet_envs`
This project requires [pybullet-gym](https://github.com/benelot/pybullet-gym/) which must be installed along side this package.
### Envs:
* AntGatherBulletEnv-v0
* AntMazeBulletEnv-v0
* AntMjBulletEnv-0
* AntFlagrunBulletEnv-v0
* PointGatherBulletEnv-v0
### Example
Also see [this notebook](https://colab.research.google.com/drive/17FX7UM1-DDb3oxg1ei64dw9Xa6JFE_zF?usp=sharing)
```
import hrl_pybullet_envs
import gym
import numpy as np
env = gym.make('AntGatherBulletEnv-v0')
env.render()
ob = env.reset()
tot_rew = 0
for i in range(1000):
# Take random actions
ob, rew, done, _ = env.step(np.random.uniform(-1, 1, env.action_space.shape))
tot_rew += rew
if done: break
print(f'Achieved total reward of: {tot_rew}')
```
%package -n python3-hrl-pybullet-envs
Summary: Locomotion HRL envs in pybullet
Provides: python-hrl-pybullet-envs
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-hrl-pybullet-envs
## Hierarchical Reinforcement envs in pybullet
This package was created because all of the HRL locomotion envs are only available in mujoco. This is an implementation of as many as possible in pybullet.
### Install
`pip install pybullet hrl_pybullet_envs`
This project requires [pybullet-gym](https://github.com/benelot/pybullet-gym/) which must be installed along side this package.
### Envs:
* AntGatherBulletEnv-v0
* AntMazeBulletEnv-v0
* AntMjBulletEnv-0
* AntFlagrunBulletEnv-v0
* PointGatherBulletEnv-v0
### Example
Also see [this notebook](https://colab.research.google.com/drive/17FX7UM1-DDb3oxg1ei64dw9Xa6JFE_zF?usp=sharing)
```
import hrl_pybullet_envs
import gym
import numpy as np
env = gym.make('AntGatherBulletEnv-v0')
env.render()
ob = env.reset()
tot_rew = 0
for i in range(1000):
# Take random actions
ob, rew, done, _ = env.step(np.random.uniform(-1, 1, env.action_space.shape))
tot_rew += rew
if done: break
print(f'Achieved total reward of: {tot_rew}')
```
%package help
Summary: Development documents and examples for hrl-pybullet-envs
Provides: python3-hrl-pybullet-envs-doc
%description help
## Hierarchical Reinforcement envs in pybullet
This package was created because all of the HRL locomotion envs are only available in mujoco. This is an implementation of as many as possible in pybullet.
### Install
`pip install pybullet hrl_pybullet_envs`
This project requires [pybullet-gym](https://github.com/benelot/pybullet-gym/) which must be installed along side this package.
### Envs:
* AntGatherBulletEnv-v0
* AntMazeBulletEnv-v0
* AntMjBulletEnv-0
* AntFlagrunBulletEnv-v0
* PointGatherBulletEnv-v0
### Example
Also see [this notebook](https://colab.research.google.com/drive/17FX7UM1-DDb3oxg1ei64dw9Xa6JFE_zF?usp=sharing)
```
import hrl_pybullet_envs
import gym
import numpy as np
env = gym.make('AntGatherBulletEnv-v0')
env.render()
ob = env.reset()
tot_rew = 0
for i in range(1000):
# Take random actions
ob, rew, done, _ = env.step(np.random.uniform(-1, 1, env.action_space.shape))
tot_rew += rew
if done: break
print(f'Achieved total reward of: {tot_rew}')
```
%prep
%autosetup -n hrl-pybullet-envs-0.2.37
%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-hrl-pybullet-envs -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.37-1
- Package Spec generated
|