openai-car.py

import gym
import matplotlib.pyplot as plt 
import time 

env = gym.make('MountainCar-v0')


# # Observation and action space 
# obs_space = env.observation_space
# action_space = env.action_space
# print("The observation space: {}".format(obs_space))
# print("The action space: {}".format(action_space))


# # reset the environment and see the initial observation
# obs = env.reset()
# print("The initial observation is {}".format(obs))

# # Sample a random action from the entire action space
# random_action = env.action_space.sample()

# # # Take the action and get the new observation space
# new_obs, reward, done, info = env.step(random_action)
# print("The new observation is {}".format(new_obs))

# #render ss
# env_screen = env.render(mode="rgb_array")
# env.close()
# plt.imshow(env_screen)
# plt.show()

# Number of steps you run the agent for 
num_steps = 1500

obs = env.reset()

for step in range(num_steps):
    # take random action, but you can also do something more intelligent
    # action = my_intelligent_agent_fn(obs) 
    action = env.action_space.sample()
    
    # apply the action
    obs, reward, done, info = env.step(action)
    
    # Render the env
    env.render()

    # Wait a bit before the next frame unless you want to see a crazy fast video
    time.sleep(0.001)
    
    # If the epsiode is up, then start another one
    if done:
        env.reset()

# Close the env
env.close()




type(env.observation_space)