Function run

tensorflow_agent/agent.py:255–338

(experiment, env_id='DeepDrivePreproTensorflow-v0', should_record=False, net_path=None, should_benchmark=True,
        run_baseline_agent=False, camera_rigs=None, should_rotate_sim_types=False,
        should_record_recovery_from_random_actions=False, render=False, path_follower=False, fps=c.DEFAULT_FPS)

def run(experiment, env_id='DeepDrivePreproTensorflow-v0', should_record=False, net_path=None, should_benchmark=True,
        run_baseline_agent=False, camera_rigs=None, should_rotate_sim_types=False,
        should_record_recovery_from_random_actions=False, render=False, path_follower=False, fps=c.DEFAULT_FPS):
    if run_baseline_agent:
        net_path = ensure_baseline_weights(net_path)
    reward = 0
    episode_done = False
    max_episodes = 1000
    tf_config = tf.ConfigProto(
        gpu_options=tf.GPUOptions(
            per_process_gpu_memory_fraction=0.8,
            # leave room for the game,
            # NOTE: debugging python, i.e. with PyCharm can cause OOM errors, where running will not
            allow_growth=True
        ),
    )
    sess = tf.Session(config=tf_config)
    if camera_rigs:
        cameras = camera_rigs[0]
    else:
        cameras = None

    if should_record and camera_rigs is not None and len(camera_rigs) >= 1:
        should_rotate_camera_rigs = True
    else:
        should_rotate_camera_rigs = False

    if should_rotate_camera_rigs:
        randomize_cameras(cameras)

    use_sim_start_command_first_lap = c.SIM_START_COMMAND is not None
    gym_env = deepdrive.start(experiment, env_id, should_benchmark=should_benchmark, cameras=cameras,
                                  use_sim_start_command=use_sim_start_command_first_lap, render=render,
                                  fps=fps)
    dd_env = gym_env.env

    # Perform random actions to reduce sampling error in the recorded dataset
    agent = Agent(gym_env.action_space, sess, env=gym_env.env,
                  should_record_recovery_from_random_actions=should_record_recovery_from_random_actions,
                  should_record=should_record, net_path=net_path, random_action_count=4, non_random_action_count=5,
                  path_follower=path_follower)
    if net_path:
        log.info('Running tensorflow agent checkpoint: %s', net_path)

    def close():
        gym_env.close()
        agent.close()

    session_done = False
    episode = 0
    try:
        while not session_done:
            if episode_done:
                obz = gym_env.reset()
                episode_done = False
            else:
                obz = None
            while not episode_done: