gym_interface.py

# coding: utf-8

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import numpy as np
import matplotlib.pyplot as plt
get_ipython().magic('matplotlib inline')

# This code creates a virtual display to draw game images on. 
# If you are running locally, just ignore it
import os
if type(os.environ.get("DISPLAY")) is not str or len(os.environ.get("DISPLAY")) == 0:
    get_ipython().system('bash ../xvfb start')
    os.environ['DISPLAY'] = ':1'


# ### OpenAI Gym
# 
# We're gonna spend several next weeks learning algorithms that solve decision processes. We are then in need of some interesting decision problems to test our algorithms.
# 
# That's where OpenAI gym comes into play. It's a python library that wraps many classical decision problems including robot control, videogames and board games.
# 
# So here's how it works:

# In[18]:


import gym
env = gym.make("MountainCar-v0")
env.reset()

plt.imshow(env.render('rgb_array'))
print("Observation space:", env.observation_space)
print("Action space:", env.action_space)


# Note: if you're running this on your local machine, you'll see a window pop up with the image above. Don't close it, just alt-tab away.

# ### Gym interface
# 
# The three main methods of an environment are
# * __reset()__ - reset environment to initial state, _return first observation_
# * __render()__ - show current environment state (a more colorful version :) )
# * __step(a)__ - commit action __a__ and return (new observation, reward, is done, info)
#  * _new observation_ - an observation right after commiting the action __a__
#  * _reward_ - a number representing your reward for commiting action __a__
#  * _is done_ - True if the MDP has just finished, False if still in progress
#  * _info_ - some auxilary stuff about what just happened. Ignore it ~~for now~~.

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obs0 = env.reset()
print("initial observation code:", obs0)

# Note: in MountainCar, observation is just two numbers: car position and velocity


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print("taking action 2 (right)")
new_obs, reward, is_done, _ = env.step(2)

print("new observation code:", new_obs)
print("reward:", reward)
print("is game over?:", is_done)

# Note: as you can see, the car has moved to the riht slightly (around 0.0005)


# ### Play with it
# 
# Below is the code that drives the car to the right. 
# 
# However, it doesn't reach the flag at the far right due to gravity. 
# 
# __Your task__ is to fix it. Find a strategy that reaches the flag. 
# 
# You're not required to build any sophisticated algorithms for now, feel free to hard-code :)
# 
# _Hint: your action at each step should depend either on __t__ or on __s__._

# In[24]:



# create env manually to set time limit. Please don't change this.
TIME_LIMIT = 250
env = gym.wrappers.TimeLimit(gym.envs.classic_control.MountainCarEnv(),
                             max_episode_steps=TIME_LIMIT + 1)
s = env.reset()
actions = {'left': 0, 'stop': 1, 'right': 2}

# prepare "display"
get_ipython().magic('matplotlib notebook')
fig = plt.figure()
ax = fig.add_subplot(111)
fig.show()

def policy(s, t):
    # YOUR CODE HERE
    print(s[1])
    if (s[1] > 0):
        return actions['right']
    else:
        return actions['left']


for t in range(TIME_LIMIT):
    
    s, r, done, _ = env.step(policy(s, t))
    
    #draw game image on display
    ax.clear()
    ax.imshow(env.render('rgb_array'))
    fig.canvas.draw()
    
    if done:
        print("Well done!")
        break
else:    
    print("Time limit exceeded. Try again.")


# ### Submit to coursera

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from submit import submit_interface
submit_interface(policy, <EMAIL>, <TOKEN>)


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