Some basic examples for reinforcement learning

Installing Anaconda and OpenAI gym

• Download and install Anaconda here
• Install OpenAI gym

pip install gym[all]==0.18.0

Examples

• Play with the environment

import gym
env = gym.make('CartPole-v0')
env.reset()
for _ in range(1000):
    env.render()
    env.step(env.action_space.sample()) # take a random action
env.close()

• Random play with CartPole-v0

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)
env.close()

• Example code for random playing (Pong-ram-v0,Acrobot-v1,Breakout-v0)

python my_random_agent.py Pong-ram-v0

• Very naive learnable agent playing CartPole-v0 or Acrobot-v1

python my_learning_agent.py CartPole-v0

• Playing Pong on CPU (with a great blog). One pretrained model is pong_model_bolei.p(after training 20,000 episodes), which you can load in by replacing save_file in the script.

python pg-pong.py

• Random navigation agent in AI2THOR

python navigation_agent.py