def make_env(obs_converter, action_converter, port, id, seed, subset,
video_every, video_dir, reward_class_name, experiment_suite,
benchmark, city_name):
return lambda: CarlaEnv(obs_converter,
action_converter,
id,
seed,
reward_class_name=reward_class_name,
port=port,
subset=subset,
video_every=video_every,
video_dir=video_dir,
exp_suite_name=experiment_suite,
benchmark=benchmark,
city_name=city_name)
env_name = "carla_env"
env_config = ENV_CONFIG.copy()
env_config.update({
"verbose": False,
"x_res": 80,
"y_res": 80,
"use_depth_camera": False,
"discrete_actions": True,
"server_map": "/Game/Maps/Town02",
"reward_function": "lane_keep",
"enable_planner": False,
"scenarios": [LANE_KEEP],
})
register_env(env_name, lambda env_config: CarlaEnv(env_config))
register_carla_model()
ray.init()
run_experiments({
"carla-dqn": {
"run": "DQN",
"env": "carla_env",
"config": {
"env_config": env_config,
"model": {
"custom_model": "carla",
"custom_options": {
"image_shape": [80, 80, 6],
},
"conv_filters": [
def update_epsilon(self):
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def exit(self):
self.logger.save_fit_history(self.dqnetwork.loss_hist,
self.dqnetwork.acc_hist)
self.logger.save_model_weights(self.dqnetwork.model, "_exit")
if __name__ == '__main__':
prepare_environment()
logger = Logger()
env = CarlaEnv(spawn_index=0, action_size=5)
state = env.reset()
agent = DQNAgent(state.shape, 5, logger)
try:
max_episodes = 10000
max_frames = 5000
total_rewards = []
epsilons = []
mean_max_q_values = []
for episode in range(max_episodes):
spawn_index = random.randint(0, len(env.spawn_points) - 1)
state = env.reset(spawn_index)
state = np.expand_dims(state, axis=0)
total_reward = 0
