Implement train_pybullet_racecar.py and enjoy_pybullet_racecar.py using OpenAI baselines DQN for the RacecarGymEnv.

examples/pybullet/gym/train_pybullet_racecar.py

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+import gym
+from envs.bullet.racecarGymEnv import RacecarGymEnv
+
+from baselines import deepq
+
+import datetime
+
+
+
+def callback(lcl, glb):
+    # stop training if reward exceeds 199
+    is_solved = lcl['t'] > 100 and sum(lcl['episode_rewards'][-101:-1]) / 100 >= 199
+    #uniq_filename = "racecar_model" + str(datetime.datetime.now().date()) + '_' + str(datetime.datetime.now().time()).replace(':', '.')
+    #print("uniq_filename=")
+    #print(uniq_filename)
+    #act.save(uniq_filename)
+    return is_solved
+
+
+def main():
+  
+    env = RacecarGymEnv(render=False)
+    model = deepq.models.mlp([64])
+    act = deepq.learn(
+        env,
+        q_func=model,
+        lr=1e-3,
+        max_timesteps=10000000,
+        buffer_size=50000,
+        exploration_fraction=0.1,
+        exploration_final_eps=0.02,
+        print_freq=10,
+        callback=callback
+    )
+    print("Saving model to racecar_model.pkl")
+    act.save("racecar_model.pkl")
+
+
+if __name__ == '__main__':
+    main()