Play with the environment and visualize the agent behaviour
import gymnasium as gym
render = True # switch if visualize the agent
if render:
env = gym.make('CartPole-v0', render_mode='human')
else:
env = gym.make('CartPole-v0')
env.reset(seed=0)
for _ in range(1000):
env.step(env.action_space.sample()) # take a random action
env.close()
Random play with CartPole-v0
import gymnasium as gym
env = gym.make('CartPole-v0')
for i_episode in range(20):
observation = env.reset()
for t in range(100):
print(observation)
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
done = np.logical_or(terminated, truncated)
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.