helper scripts for loading and testing stuff

plot_loader.py

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+import os
+import sys
+import numpy as np
+import matplotlib.pyplot as plt
+from stable_baselines.results_plotter import load_results, ts2xy
+
+
+def moving_average(values, window):
+    """
+    Smooth values by doing a moving average
+    :param values: (numpy array)
+    :param window: (int)
+    :return: (numpy array)
+    """
+    weights = np.repeat(1.0, window) / window
+    return np.convolve(values, weights, 'valid')
+
+
+def plot_results(log_folder, model_name, plt_dir, title='Learning Curve'):
+    """
+    plot the results
+
+    :param log_folder: (str) the save location of the results to plot
+    :param title: (str) the title of the task to plot
+    """
+    # m_name_csv = model_name + ".csv"
+    # old_file_name = os.path.join(log_folder, "monitor.csv")
+    # new_file_name = os.path.join(log_folder, m_name_csv)
+    save_name = os.path.join(plt_dir, model_name)
+
+    x, y = ts2xy(load_results(log_folder), 'timesteps')
+    # shutil.copy(old_file_name, new_file_name)
+    y = moving_average(y, window=10)
+    # Truncate x
+    x = x[len(x) - len(y):]
+
+    fig = plt.figure(title)
+    plt.plot(x, y)
+    plt.xlabel('Number of Timesteps')
+    plt.ylabel('Rewards')
+    plt.title(title + " Smoothed")
+    print('Saving plot at:', save_name)
+    plt.savefig(save_name + ".png")
+    plt.savefig(save_name + ".eps")
+    print("plots saved...")
+    # plt.show()
+
+if __name__ == '__main__':
+    plot_results(str(sys.argv[1]), str(sys.argv[2]), str(sys.argv[3]))

stand_loader.py

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+import subprocess
+import sys
+
+if __name__ == "__main__":
+    env_name = str(sys.argv[1])
+    model_name = str(sys.argv[2])
+
+    subprocess.Popen(
+        '''export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so:/usr/lib/nvidia-410/libGL.so; python load_agent.py '%s' '%s' ''' % (env_name, model_name), shell=True)

test.py

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+import gym
+
+from stable_baselines.common.policies import MlpPolicy
+from stable_baselines.common.vec_env import SubprocVecEnv
+from stable_baselines import PPO2
+
+# multiprocess environment
+n_cpu = 20
+env = SubprocVecEnv([lambda: gym.make('CartPole-v1') for i in range(n_cpu)])
+
+model = PPO2(MlpPolicy, env, verbose=1)
+model.learn(total_timesteps=25000)
+model.save("ppo2_cartpole")
+
+del model # remove to demonstrate saving and loading
+
+model = PPO2.load("ppo2_cartpole")
+
+# Enjoy trained agent
+obs = env.reset()
+for _ in range(1000):
+    action, _states = model.predict(obs)
+    obs, rewards, dones, info = env.step(action)
+    # env.render()
+
+env.close()