openai-gym

来源： https://stackoverflow.com/questions/54047654/tensorflow-different-results-with-the-same-random-seed

来源： https://stackoverflow.com/questions/40509002/ffmpeg-is-not-being-detected-by-spyder

问题 Rllib docs provide some information about how to create and train a custom environment. There is some information about registering that environment, but I guess it needs to work differently than gym registration. I'm testing this out working with the SimpleCorridor environment. If I add the registration code to the file like so: from ray.tune.registry import register_env class SimpleCorridor(gym.Env): ... def env_creator(env_config): return SimpleCorridor(env_config) register_env("corridor",

问题 There are some things that I would like to modify in the OpenAI environments. If we use the Cartpole example then we can edit things that are in the class init function but with environments that use Box2D it doesn't seem to be as straight forward? For example, consider the BipedalWalker environment. In this case how would I edit things like the SPEED_HIP or SPEED_KNEE variables? Thanks 回答1: Yes, you can modify or create new environments in gym. The simplest (but not recommended) way is to

问题 There are some things that I would like to modify in the OpenAI environments. If we use the Cartpole example then we can edit things that are in the class init function but with environments that use Box2D it doesn't seem to be as straight forward? For example, consider the BipedalWalker environment. In this case how would I edit things like the SPEED_HIP or SPEED_KNEE variables? Thanks 回答1: Yes, you can modify or create new environments in gym. The simplest (but not recommended) way is to

问题 Me and my classmate have decided to try and implement and AI agent into our own game. My friend have done most of the code, based on previous projects, and I was wondering how PyGame and OpenAI would work together. Have tried to do some research but can't really find any useful information about this specific topic. Some have said that it is hard to implement but some say it works. Either way, I'd like your opinion on this project and how you'd approach this if it was you. The game is very

问题 Me and my classmate have decided to try and implement and AI agent into our own game. My friend have done most of the code, based on previous projects, and I was wondering how PyGame and OpenAI would work together. Have tried to do some research but can't really find any useful information about this specific topic. Some have said that it is hard to implement but some say it works. Either way, I'd like your opinion on this project and how you'd approach this if it was you. The game is very

问题 whenever I try to run my program on WSL, I get the following error. I'm pretty new to pytorch and vizdoom, so I don't know how to solve this problem. Setup - Windows 10 x64 - Ubuntu 14 (on WSL) - Python 2.7.14 (Anaconda 2) - OpenAI Gym 0.9.5 - Vizdoom 1.1.4 - doom-py 0.0.14 - ppaquette/gym-doom - pytorch 0.0.12 (doomenv) hybridsyntax@Blacklynx:/mnt/f/_TUTORIALS/ai/doom/code$ python ai.py > wsl.log [2018-05-25 18:21:44,354] Making new env: ppaquette/DoomCorridor-v0 [2018-05-25 18:21:44,365]

问题 I've just installed openAI gym on Google Colab, but when I try to run 'CartPole-v0' environment as explained here. Code: import gym env = gym.make('CartPole-v0') for i_episode in range(20): observation = env.reset() for t in range(100): env.render() print(observation) action = env.action_space.sample() observation, reward, done, info = env.step(action) if done: print("Episode finished after {} timesteps".format(t+1)) break I get this: WARN: gym.spaces.Box autodetected dtype as <class 'numpy

问题 I am trying to use the famous 'Gym' module from OpenAI on WSL and executing code on python 3.5.2. When I try to run an environment as explained here, using the code: import gym env = gym.make('CartPole-v0') for i_episode in range(20): observation = env.reset() for t in range(100): env.render() print(observation) action = env.action_space.sample() observation, reward, done, info = env.step(action) if done: print("Episode finished after {} timesteps".format(t+1)) break this happens : Traceback