conda activate ad cd /home/用户名/anaconda3/envs/ad/gym/gym_torcs python snakeoil3_gym.py 这时画面又开始运行了,如下图 到这就算是安装成功了,在/home/用户名/anaconda3/envs/ad/gym/gym_torcs这个文件夹里还有一个example_experiment.py,也可以运行一下,直接在终端输入 python example_experiment.py ...
torcs_action = {'steer': u[0]} if self.throttle is True: # throttle action is enabled torcs_action.update({'accel': u[1]}) torcs_action.update({'brake': u[2]}) if self.gear_change is True: # gear change action is enabled torcs_action.update({'gear': int(u[3])}) return ...
安装OpenAI-Gym:https://github.com/openai/gym sudo apt-get install -y python-numpy python-dev cmake zlib1g-dev libjpeg-dev xvfb libav-tools xorg-dev python-opengl libboost-all-dev libsdl2-dev swig sudo pip install'gym[all]' 安装Gym-TORCS:https://github.com/ugo-nama-kun/gym_torcs git ...
fromgym_torcsimportTorcsEnv### Generate a Torcs environment# enable vision input, the action is steering only (1 dim continuous action)env=TorcsEnv(vision=True,throttle=False)# without vision input, the action is steering and throttle (2 dim continuous action)# env = TorcsEnv(vision=False, ...
gym_torcs.py practice.xml sample_agent.py snakeoil3_gym.py Repository files navigation README MIT license Gym-TORCS Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with OpenAI-gym-like interface. TORCS is the open-rource realistic car racing simulator recently used...
TORCS(The Open Racing Car Simulator)是一个跨平台的赛车游戏模拟器,也可作为强化学习的研究平台。官方网站:http://torcs.sourceforge.net/。但我们不需直接从官网下。gym_torcs是一个TORCS的强化学习环境,提供类似前面OpenAI Gym的接口,网站为https://github.com/ugo-nama-kun/gym_torcs。
TORCS TORCS(The Open Racing Car Simulator)是一个跨平台的赛车游戏模拟器,也可作为强化学习的研究平台。官方网站:http://torcs.sourceforge.net/。但我们不需直接从官网下。gym_torcs是一个TORCS的强化学习环境,提供类似前面OpenAI Gym的接口,网站为https://github.com/ugo-nama-kun/gym_torcs。
TORCS(The Open Racing Car Simulator)是一个跨平台的赛车游戏模拟器,也可作为强化学习的研究平台。官方网站:http://torcs.sourceforge.net/。但我们不需直接从官网下。gym_torcs是一个TORCS的强化学习环境,提供类似前面OpenAI Gym的接口,网站为https://github.com/ugo-nama-kun/gym_torcs。
A fork of ugo-nama-kun's gym_torcs environment with humble improvements such as: Removing the need for xautomation: the environment can be started virtually headlessly, skipping the GUI part. Wrapper following the OpenAI Gym standard for environments: you can now instantiate the environment using...
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