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main_rev3_4.py
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from starcraft2_rev3 import StarCraft2Env
from GDN_rev3 import Agent
import numpy as np
import torch
import pickle
import gc
import pandas as pd
from pysc2.lib.remote_controller import ConnectError, RequestError
from pysc2.lib.protocol import ProtocolError
from functools import partial
import sys
import os
import torch
from torch.utils.tensorboard import SummaryWriter
from functools import partial
import sys
import os
import vessl
vessl.init()
def env_fn(env, **kwargs):
return env(**kwargs)
REGISTRY = {}
REGISTRY["sc2_temp"] = partial(env_fn, env=StarCraft2Env)
REGISTRY["sc2"] = partial(env_fn, env=StarCraft2Env)
if sys.platform == "linux":
os.environ.setdefault("SC2PATH",
os.path.join(os.getcwd(), "3rdparty", "StarCraftII"))
regularizer = 0.0
map_name1 = '3s5z_vs_3s6z'
"""
Protoss
colossi : 200.0150.01.0
stalkers : 80.080.00.625
zealots : 100.050.00.5
Terran
medivacs : 150.00.00.75
marauders : 125.00.00.5625
marines : 45.00.00.375
Zerg
zergling : 35.00.00.375
hydralisk : 80.00.00.625
baneling : 30.00.00.375
spine crawler : 300.00.01.125
"""
def evaluation(env, agent, num_eval):
max_episode_len = env.episode_limit
t = 0
win_rates = 0
for e in range(num_eval):
env.reset()
done = False
episode_reward = 0
step = 0
while (not done) and (step < max_episode_len):
step += 1
node_feature = env.get_graph_feature()
edge_index_enemy = env.get_enemy_visibility_edge_index()
edge_index_ally = env.get_ally_visibility_edge_index()
n_node_features = torch.tensor(node_feature).shape[0]
node_representation = agent.get_node_representation(node_feature, edge_index_enemy, edge_index_ally,
n_node_features,
mini_batch=False) # 차원 : n_agents X n_representation_comm
avail_action = env.get_avail_actions()
action_feature = env.get_action_feature() # 차원 : action_size X n_action_feature
action = agent.sample_action(node_representation, action_feature, avail_action, epsilon=0)
reward, done, info = env.step(action)
win_tag = True if done and 'battle_won' in info and info['battle_won'] else False
episode_reward += reward
t += 1
print("map name {} : Evaluation episode {}, episode reward {}, win_tag {}".format(env.map_name, e, episode_reward, win_tag))
if win_tag == True:
win_rates += 1 / num_eval
print("map name : ", env.map_name, "승률", win_rates)
return win_rates
def network_sharing(agent_group):
agent_ref = agent_group[0]
for agent in agent_group[1:]:
agent.VDN = agent_ref.VDN
agent.VDN_target = agent_ref.VDN_target
agent.Q = agent_ref.Q
agent.Q_tar = agent_ref.Q_tar
agent.node_representation_enemy_obs = agent_ref.node_representation_enemy_obs
agent.node_representation = agent_ref.node_representation
agent.action_representation = agent_ref.action_representation
agent.optimizer = agent_ref.optimizer
def get_agent_type_of_envs(envs):
agent_type_ids = list()
type_alliance = list()
for env in envs:
for agent_id, _ in env.agents.items():
agent = env.get_unit_by_id(agent_id)
agent_type_ids.append(str(agent.health_max)+str(agent.shield_max)+str(agent.radius))
type_alliance.append([str(agent.health_max)+str(agent.shield_max)+str(agent.radius), agent.alliance])
for e_id, e_unit in env.enemies.items():
enemy = list(env.enemies.items())[e_id][1]
agent_type_ids.append(str(enemy.health_max)+str(enemy.shield_max)+str(enemy.radius))
type_alliance.append([str(enemy.health_max)+str(enemy.shield_max)+str(enemy.radius), enemy.alliance])
agent_types_list = list(set(agent_type_ids))
type_alliance_set = list()
for x in type_alliance:
if x not in type_alliance_set:
type_alliance_set.append(x)
print(type_alliance_set)
for id in agent_types_list:
print("id : ", id, "count : " , agent_type_ids.count(id))
return len(agent_types_list), agent_types_list
def train(agent, env, e, t, train_start, epsilon, min_epsilon, anneal_epsilon):
max_episode_limit = env.episode_limit
env.reset()
done = False
episode_reward = 0
step = 0
losses = []
eval = False
while (not done) and (step < max_episode_limit):
node_feature = env.get_graph_feature()
edge_index_enemy = env.get_enemy_visibility_edge_index()
edge_index_ally = env.get_ally_visibility_edge_index()
n_node_features = torch.tensor(node_feature).shape[0]
node_representation = agent.get_node_representation(node_feature, edge_index_enemy, edge_index_ally,
n_node_features,
mini_batch=False) # 차원 : n_agents X n_representation_comm
avail_action = env.get_avail_actions()
action_feature = env.get_action_feature() # 차원 : action_size X n_action_feature
action = agent.sample_action(node_representation, action_feature, avail_action, epsilon)
reward, done, info = env.step(action)
agent.buffer.memory(node_feature, action, action_feature, edge_index_enemy, edge_index_ally, reward,
done, avail_action)
episode_reward += reward
t += 1
step += 1
if (t % 5000 == 0) and (t >0):
eval = True
if e >= train_start:
loss = agent.learn(regularizer)
losses.append(loss.detach().item())
if epsilon >= min_epsilon:
epsilon = epsilon - anneal_epsilon
else:
epsilon = min_epsilon
if e >= train_start:
print("{} Total reward in episode {} = {}, loss : {}, epsilon : {}, time_step : {}".format(env.map_name,
e,
episode_reward,
loss,
epsilon,
t))
return episode_reward, epsilon, t, eval
def main():
env1_temp = REGISTRY["sc2_temp"](map_name=map_name1, seed=123, step_mul = 8)
env1_temp.reset()
num_unit_types, unit_type_ids = get_agent_type_of_envs([env1_temp])
env1_temp.generate_num_unit_types(num_unit_types, unit_type_ids)
hidden_size_obs = 54
hidden_size_comm = 64
hidden_size_Q = 84
n_representation_obs = 56
n_representation_comm = 64
buffer_size = 150000
batch_size = 32
gamma = 0.99
learning_rate = 2e-4
n_multi_head = 1
dropout = 0.6
num_episode = 1000000
train_start = 10
epsilon = 1
min_epsilon = 0.05
anneal_steps = 50000
anneal_epsilon = (epsilon - min_epsilon) / anneal_steps
agent1 = Agent(num_agent=env1_temp.get_env_info()["n_agents"],
feature_size=env1_temp.get_env_info()["node_features"],
hidden_size_obs=hidden_size_obs,
hidden_size_comm=hidden_size_comm,
hidden_size_Q=hidden_size_Q,
n_multi_head=n_multi_head,
n_representation_obs=n_representation_obs,
n_representation_comm=n_representation_comm,
dropout=dropout,
action_size=env1_temp.get_env_info()["n_actions"],
buffer_size=buffer_size,
batch_size=batch_size,
max_episode_len=env1_temp.episode_limit,
learning_rate=learning_rate,
gamma=gamma)
# agent2 = Agent(num_agent=env2.get_env_info()["n_agents"],
# feature_size=env2.get_env_info()["node_features"],
# hidden_size_obs=hidden_size_obs,
# hidden_size_comm=hidden_size_comm,
# hidden_size_Q=hidden_size_Q,
# n_multi_head=n_multi_head,
# n_representation_obs=n_representation_obs,
# n_representation_comm=n_representation_comm,
# dropout=dropout,
# action_size=env2.get_env_info()["n_actions"],
# buffer_size=buffer_size,
# batch_size=batch_size,
# max_episode_len=env2.episode_limit,
# learning_rate=learning_rate,
# gamma=gamma)
env1_temp.close()
#network_sharing([agent1])
t = 0
env1 = REGISTRY["sc2"](map_name=map_name1, seed=123, step_mul = 8)
env1.generate_num_unit_types(num_unit_types, unit_type_ids)
epi_r = []
for e in range(num_episode):
episode_reward, epsilon, t, eval = train(agent1, env1, e, t, train_start, epsilon, min_epsilon, anneal_epsilon)
epi_r.append(episode_reward)
if e % 100 == 1:
vessl.log(step=e, payload={'reward': np.mean(epi_r)})
epi_r = []
if eval == True:
win_rate = evaluation(env1, agent1, 32)
vessl.log(step=t, payload={'win_rate': win_rate})
main()