# make env
env = Environment(MAP_PATH,
red_agent_obs_ind,
blue_agent_obs_ind,
max_step=1000,
render=RENDER)
# get map info
size_x, size_y = env.get_map_size()
red_detector_num, red_fighter_num, blue_detector_num, blue_fighter_num = env.get_unit_num(
)
# set map info to blue agent
blue_agent.set_map_info(size_x, size_y, blue_detector_num,
blue_fighter_num)
red_detector_action = []
fighter_model = dqn.RLFighter(ACTION_NUM)
# execution
for x in range(MAX_EPOCH):
step_cnt = 0
env.reset()
episodic_reward = 0
while True:
obs_list = []
action_list = []
red_fighter_action = []
# get obs
if step_cnt == 0:
red_obs_dict, blue_obs_dict = env.get_obs()
# get action
blue_agent_obs_ind,
render=RENDER,
max_step=MAX_STEP)
# get map info
size_x, size_y = env.get_map_size()
red_detector_num, red_fighter_num, blue_detector_num, blue_fighter_num = env.get_unit_num(
)
# set map info to blue agent
blue_agent.set_map_info(size_x, size_y, blue_detector_num,
blue_fighter_num)
red_detector_action = []
fighter_model = dqn.RLFighter(n_actions=ACTION_NUM,
learning_rate=LR,
reward_decay=GAMMA,
e_greedy=EPSILON,
e_greedy_increment=EPSILON_INCREMENT,
capacity=CAPACITY,
batch_size=BATCH_SIZE,
replace_target_iter=TARGET_REPLACE_ITER)
# execution
for i_episode in range(MAX_EPOCH):
step_cnt = 0
total_reward = 0.0 # 每回合所有智能体的总体奖励
env.reset()
while True:
obs_list = [0 for _ in range(red_fighter_num)]
action_list = [0 for _ in range(red_fighter_num)]
red_fighter_action = []
# get obs
if step_cnt == 0: