Date: Fri, 21 Jun 2019 23:08:45 +0000 (UTC) From: Sunpoet Po-Chuan Hsieh <sunpoet@FreeBSD.org> To: ports-committers@freebsd.org, svn-ports-all@freebsd.org, svn-ports-head@freebsd.org Subject: svn commit: r504818 - in head/math: . py-gym Message-ID: <201906212308.x5LN8jtm061852@repo.freebsd.org>
next in thread | raw e-mail | index | archive | help
Author: sunpoet Date: Fri Jun 21 23:08:45 2019 New Revision: 504818 URL: https://svnweb.freebsd.org/changeset/ports/504818 Log: Add py-gym 0.12.5 OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This is the gym open-source library, which gives you access to a standardized set of environments. gym makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages. There are two basic concepts in reinforcement learning: the environment (namely, the outside world) and the agent (namely, the algorithm you are writing). The agent sends actions to the environment, and the environment replies with observations and rewards (that is, a score). The core gym interface is Env, which is the unified environment interface. There is no interface for agents; that part is left to you. The following are the Env methods you should know: - reset(self): Reset the environment's state. Returns observation. - step(self, action): Step the environment by one timestep. Returns observation, reward, done, info. - render(self, mode='human'): Render one frame of the environment. The default mode will do something human friendly, such as pop up a window. WWW: https://gym.openai.com/ WWW: https://github.com/openai/gym Added: head/math/py-gym/ head/math/py-gym/Makefile (contents, props changed) head/math/py-gym/distinfo (contents, props changed) head/math/py-gym/pkg-descr (contents, props changed) Modified: head/math/Makefile Modified: head/math/Makefile ============================================================================== --- head/math/Makefile Fri Jun 21 23:08:38 2019 (r504817) +++ head/math/Makefile Fri Jun 21 23:08:45 2019 (r504818) @@ -707,6 +707,7 @@ SUBDIR += py-gnuplot SUBDIR += py-grandalf SUBDIR += py-graphillion + SUBDIR += py-gym SUBDIR += py-igakit SUBDIR += py-igraph SUBDIR += py-intspan Added: head/math/py-gym/Makefile ============================================================================== --- /dev/null 00:00:00 1970 (empty, because file is newly added) +++ head/math/py-gym/Makefile Fri Jun 21 23:08:45 2019 (r504818) @@ -0,0 +1,27 @@ +# Created by: Po-Chuan Hsieh <sunpoet@FreeBSD.org> +# $FreeBSD$ + +PORTNAME= gym +PORTVERSION= 0.12.5 +CATEGORIES= math python +MASTER_SITES= CHEESESHOP +PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} + +MAINTAINER= sunpoet@FreeBSD.org +COMMENT= OpenAI toolkit for developing and comparing your reinforcement learning agents + +LICENSE= MIT + +RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}numpy>=1.10.4:math/py-numpy@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}pyglet>=0:graphics/py-pyglet@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}scipy>=0:science/py-scipy@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}six>=0:devel/py-six@${PY_FLAVOR} +TEST_DEPENDS= ${PYTHON_PKGNAMEPREFIX}mock>=0:devel/py-mock@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}pytest>=0:devel/py-pytest@${PY_FLAVOR} + +USES= python +USE_PYTHON= autoplist concurrent distutils + +NO_ARCH= yes + +.include <bsd.port.mk> Added: head/math/py-gym/distinfo ============================================================================== --- /dev/null 00:00:00 1970 (empty, because file is newly added) +++ head/math/py-gym/distinfo Fri Jun 21 23:08:45 2019 (r504818) @@ -0,0 +1,3 @@ +TIMESTAMP = 1561148961 +SHA256 (gym-0.12.5.tar.gz) = 027422f59b662748eae3420b804e35bbf953f62d40cd96d2de9f842c08de822e +SIZE (gym-0.12.5.tar.gz) = 1544308 Added: head/math/py-gym/pkg-descr ============================================================================== --- /dev/null 00:00:00 1970 (empty, because file is newly added) +++ head/math/py-gym/pkg-descr Fri Jun 21 23:08:45 2019 (r504818) @@ -0,0 +1,24 @@ +OpenAI Gym is a toolkit for developing and comparing reinforcement learning +algorithms. This is the gym open-source library, which gives you access to a +standardized set of environments. + +gym makes no assumptions about the structure of your agent, and is compatible +with any numerical computation library, such as TensorFlow or Theano. You can +use it from Python code, and soon from other languages. + +There are two basic concepts in reinforcement learning: the environment (namely, +the outside world) and the agent (namely, the algorithm you are writing). The +agent sends actions to the environment, and the environment replies with +observations and rewards (that is, a score). + +The core gym interface is Env, which is the unified environment interface. There +is no interface for agents; that part is left to you. The following are the Env +methods you should know: +- reset(self): Reset the environment's state. Returns observation. +- step(self, action): Step the environment by one timestep. Returns observation, + reward, done, info. +- render(self, mode='human'): Render one frame of the environment. The default + mode will do something human friendly, such as pop up a window. + +WWW: https://gym.openai.com/ +WWW: https://github.com/openai/gym
Want to link to this message? Use this URL: <https://mail-archive.FreeBSD.org/cgi/mid.cgi?201906212308.x5LN8jtm061852>