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run_log.py
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run_log.py
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# -*- coding:utf-8 -*-
import os
import time
import json
import numpy as np
import argparse
import sys
sys.path.append("./olympics_engine")
from env.chooseenv import make
from utils.get_logger import get_logger
from env.obs_interfaces.observation import obs_type
class NpEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(NpEncoder, self).default(obj)
def get_players_and_action_space_list(g):
if sum(g.agent_nums) != g.n_player:
raise Exception("agent number = %d 不正确，与n_player = %d 不匹配" % (sum(g.agent_nums), g.n_player))
n_agent_num = list(g.agent_nums)
for i in range(1, len(n_agent_num)):
n_agent_num[i] += n_agent_num[i - 1]
# 根据agent number 分配 player id
players_id = []
actions_space = []
for policy_i in range(len(g.obs_type)):
if policy_i == 0:
players_id_list = range(n_agent_num[policy_i])
else:
players_id_list = range(n_agent_num[policy_i - 1], n_agent_num[policy_i])
players_id.append(players_id_list)
action_space_list = [g.get_single_action_space(player_id) for player_id in players_id_list]
actions_space.append(action_space_list)
#Box(low=-100, high=200.0, shape=(1,), dtype=np.float32)
#Box(low=-30, high=30.0, shape=(1,), dtype=np.float32)
return players_id, actions_space
def get_joint_action_eval(game, multi_part_agent_ids, policy_list, actions_spaces, all_observes):
if len(policy_list) != len(game.agent_nums):
error = "模型个数%d与玩家个数%d维度不正确！" % (len(policy_list), len(game.agent_nums))
raise Exception(error)
# [[[0, 0, 0, 1]], [[0, 1, 0, 0]]]
joint_action = []
for policy_i in range(len(policy_list)):
if game.obs_type[policy_i] not in obs_type:
raise Exception("可选obs类型：%s" % str(obs_type))
agents_id_list = multi_part_agent_ids[policy_i]
action_space_list = actions_spaces[policy_i]
#第一个动作范围是-100,200
#第二个动作范围是-30，30
function_name = 'm%d' % policy_i
for i in range(len(agents_id_list)):
agent_id = agents_id_list[i]
a_obs = all_observes[agent_id]
each = eval(function_name)(a_obs, action_space_list[i], game.is_act_continuous)
#print(each)
game.is_single_valid_action(each, action_space_list[i], policy_i)
joint_action.append(each)
#print(joint_action)
return joint_action
def set_seed(g, env_name):
if env_name.split("-")[0] in ['magent']:
g.reset()
seed = g.create_seed()
g.set_seed(seed)
def run_game(g, env_name, multi_part_agent_ids, actions_spaces, policy_list, render_mode):
"""
This function is used to generate log for Vue rendering. Saves .json file
"""
log_path = os.getcwd() + '/logs/'
if not os.path.exists(log_path):
os.mkdir(log_path)
logger = get_logger(log_path, g.game_name, json_file=render_mode)
set_seed(g, env_name)
#从这里导入agent的文件
for i in range(len(policy_list)):
if policy_list[i] not in get_valid_agents():
raise Exception("agent {} not valid!".format(policy_list[i]))
file_path = os.path.dirname(os.path.abspath(__file__)) + "/agents/" + policy_list[i] + "/submission.py"
if not os.path.exists(file_path):
raise Exception("file {} not exist!".format(file_path))
import_path = '.'.join(file_path.split('/')[-3:])[:-3]
function_name = 'm%d' % i
import_name = "my_controller"
import_s = "from %s import %s as %s" % (import_path, import_name, function_name)
#print(import_s)
#在这里执行
exec(import_s, globals())
st = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
game_info = {"game_name": env_name,
"n_player": g.n_player,
"board_height": g.board_height if hasattr(g, "board_height") else None,
"board_width": g.board_width if hasattr(g, "board_width") else None,
"init_info": g.init_info,
"start_time": st,
"mode": "terminal",
"seed": g.seed if hasattr(g, "seed") else None,
"map_size": g.map_size if hasattr(g, "map_size") else None}
steps = []
#初始化环境
#获取两个agent的观测
all_observes = g.all_observes
while not g.is_terminal():
step = "step%d" % g.step_cnt
if g.step_cnt % 10 == 0:
print(step)
if render_mode and hasattr(g, "env_core"):
if hasattr(g.env_core, "render"):
g.env_core.render()
info_dict = {"time": time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))}
#一个agent的动作空间是两个连续动作空间 分别是朝向和动力 连续变量
joint_act = get_joint_action_eval(g, multi_part_agent_ids, policy_list, actions_spaces, all_observes)
#done判断游戏是否结束
#info_before表示了过去两个agent的动作
#info after中有3个key 分别是agent_position、agent_direction和agent_energy agent_position表示了两个agent的位置和球的位置
#agent_direction 表示了agent和球的朝向
all_observes, reward, done, info_before, info_after = g.step(joint_act)
if env_name.split("-")[0] in ["magent"]:
info_dict["joint_action"] = g.decode(joint_act)
if info_before:
info_dict["info_before"] = info_before
info_dict["reward"] = reward
if info_after:
info_dict["info_after"] = info_after
steps.append(info_dict)
game_info["steps"] = steps
game_info["winner"] = g.check_win()
game_info["winner_information"] = g.won
game_info["n_return"] = g.n_return
ed = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
game_info["end_time"] = ed
logs = json.dumps(game_info, ensure_ascii=False, cls=NpEncoder)
logger.info(logs)
#读取agent文件夹下的具体agent目录
def get_valid_agents():
dir_path = os.path.join(os.path.dirname(__file__), 'agents')
return [f for f in os.listdir(dir_path) if f != "__pycache__"]
if __name__ == "__main__":
env_type = "olympics-tablehockey"
game = make(env_type, seed=None)
render_mode = True
parser = argparse.ArgumentParser()
parser.add_argument("--my_ai", default="random", help="random")
parser.add_argument("--opponent", default="random", help="random")
args = parser.parse_args()
# policy_list = ["random"] * len(game.agent_nums)
policy_list = [args.opponent, args.my_ai] #["random"] * len(game.agent_nums), here we control agent 2 (green agent)
multi_part_agent_ids, actions_space = get_players_and_action_space_list(game)
run_game(game, env_type, multi_part_agent_ids, actions_space, policy_list, render_mode)