class AlternateTraining(Trainable):
def _setup(self, config):
self.config = config
self.env = config['env']
agent_config = self.config
adv_config = deepcopy(self.config)
agent_config['multiagent']['policies_to_train'] = ['agent']
adv_config['multiagent']['policies_to_train'] = ['adversary0']
self.agent_trainer = PPOTrainer(env=self.env, config=agent_config)
self.adv_trainer = PPOTrainer(env=self.env, config=adv_config)
def _train(self):
# improve the Adversary policy
print("-- Adversary Training --")
print(pretty_print(self.adv_trainer.train()))
# swap weights to synchronize
self.agent_trainer.set_weights(self.adv_trainer.get_weights(["adversary0"]))
# improve the Agent policy
print("-- Agent Training --")
output = self.agent_trainer.train()
print(pretty_print(output))
# swap weights to synchronize
self.adv_trainer.set_weights(self.agent_trainer.get_weights(["agent"]))
return output
def _save(self, tmp_checkpoint_dir):
return self.agent_trainer._save(tmp_checkpoint_dir)
def my_train_fn(config, reporter):
assert args.num_learners >= 4, 'Requires 4 or more trainable agents'
ppo_trainer = PPOTrainer(env='c4', config=config)
while True:
result = ppo_trainer.train()
if 'evaluation' in result:
train_policies = config['multiagent']['policies_to_train']
scores = {k: v for k, v in result['evaluation']['policy_reward_mean'].items() if k in train_policies}
scores_dist = softmax(np.array(list(scores.values())) / tau)
new_trainables = random.choices(list(scores.keys()), scores_dist, k=len(scores))
# new_trainables = train_policies
# random.shuffle(new_trainables)
weights = ppo_trainer.get_weights()
new_weights = {old_pid: weights[new_pid] for old_pid, new_pid in zip(weights.keys(), new_trainables)}
# new_weights = {pid: np.zeros_like(wt) for pid, wt in weights.items() if wt is not None}
# new_weights = {pid: np.ones_like(wt)*-100 for pid, wt in weights.items() if wt is not None}
# new_weights = {pid: np.random.rand(*wt.shape) for pid, wt in weights.items() if wt is not None}
print('\n\n################\nSETTING WEIGHTS\n################\n\n')
ppo_trainer.set_weights(new_weights)
num_metrics = 4
c = Counter(new_trainables)
result['custom_metrics'].update(
{f'most_common{i:02d}': v[1] for i, v in enumerate(c.most_common(num_metrics))})
result['custom_metrics'].update(
{f'scores_dist{i:02d}': v for i, v in enumerate(sorted(scores_dist, reverse=True)[:num_metrics])})
print('scores_dist', scores_dist)
# result['custom_metrics'].update(
# {f'new_agent{i:02d}': int(v[-2:]) for i, v in enumerate(new_trainables)})
reporter(**result)
class AlternateTraining(Trainable):
def _setup(self, config):
self.config = config
self.env = config['env']
agent_config = self.config
adv_config = deepcopy(self.config)
agent_config['multiagent']['policies_to_train'] = ['agent']
adv_config['multiagent']['policies_to_train'] = ['adversary']
self.agent_trainer = PPOTrainer(env=self.env, config=agent_config)
self.adv_trainer = PPOTrainer(env=self.env, config=adv_config)
def _train(self):
# improve the Adversary policy
print("-- Adversary Training --")
original_weight = self.adv_trainer.get_weights(
["adversary"])['adversary']['adversary/fc_1/kernel'][0, 0]
print(pretty_print(self.adv_trainer.train()))
first_weight = self.adv_trainer.get_weights(
["adversary"])['adversary']['adversary/fc_1/kernel'][0, 0]
# Check that the weights are updating after training
assert original_weight != first_weight, 'The weight hasn\'t changed after training what gives'
# swap weights to synchronize
self.agent_trainer.set_weights(
self.adv_trainer.get_weights(["adversary"]))
# improve the Agent policy
print("-- Agent Training --")
output = self.agent_trainer.train()
# Assert that the weight hasn't changed but it has
new_weight = self.agent_trainer.get_weights(
["adversary"])['adversary']['adversary/fc_1/kernel'][0, 0]
# Check that the adversary is not being trained when the agent trainer is training
assert first_weight == new_weight, 'The weight of the adversary matrix has changed but it shouldnt have been updated!'
# swap weights to synchronize
self.adv_trainer.set_weights(self.agent_trainer.get_weights(["agent"]))
return output
def _save(self, tmp_checkpoint_dir):
return self.agent_trainer._save(tmp_checkpoint_dir)
dqn_trainer = DQNTrainer(env="multi_cartpole",
config={
"multiagent": {
"policies": policies,
"policy_mapping_fn": policy_mapping_fn,
"policies_to_train": ["dqn_policy"],
},
"gamma": 0.95,
"n_step": 3,
})
# You should see both the printed X and Y approach 200 as this trains:
# info:
# policy_reward_mean:
# dqn_policy: X
# ppo_policy: Y
for i in range(args.num_iters):
print("== Iteration", i, "==")
# improve the DQN policy
print("-- DQN --")
print(pretty_print(dqn_trainer.train()))
# improve the PPO policy
print("-- PPO --")
print(pretty_print(ppo_trainer.train()))
# swap weights to synchronize
dqn_trainer.set_weights(ppo_trainer.get_weights(["ppo_policy"]))
ppo_trainer.set_weights(dqn_trainer.get_weights(["dqn_policy"]))
config["num_sgd_iter"] = 5
config["sgd_minibatch_size"] = 8192
config["train_batch_size"] = 20000
config["use_gae"] = True
config["vf_clip_param"] = 10
config["vf_loss_coeff"] = 1
config["vf_share_layers"] = False
# For better gradient estimates in the later stages
# of the training, increase the batch sizes.
# config["sgd_minibatch_size"] = 8192 * 4
# config["train_batch_size"] = 20000 * 10
ray.init()
trainer = PPOTrainer(config=config, env=InventoryEnv)
# Use this when you want to continue from a checkpoint.
# trainer.restore(
# "/home/enes/ray_results/PPO_InventoryEnv_2020-10-06_04-31-2945lwn1wg/checkpoint_737/checkpoint-737"
# )
best_mean_reward = np.NINF
while True:
result = trainer.train()
print(pretty_print(result))
mean_reward = result.get("episode_reward_mean", np.NINF)
if mean_reward > best_mean_reward:
checkpoint = trainer.save()
print("checkpoint saved at", checkpoint)
best_mean_reward = mean_reward
import numpy as np
import matplotlib.pyplot as plt
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import PPO2, A2C
import gym
import traf_env
import multi_traf_env
import ray
from ray import tune
from ray.rllib.policy import Policy
from ray.rllib.tests.test_multi_agent_env import MultiCartpole
from ray.tune.registry import register_env
from ray.rllib.agents.ppo.ppo import PPOTrainer
from ray.rllib.agents.ppo.ppo_policy import PPOTFPolicy
ray.init()
register_env("multi_air-v0",
lambda c: multi_traf_env.AirTrafficGym(num_agents=2))
trainer = PPOTrainer(env="multi_air-v0")
num_train_itr = 50
for i in range(num_train_itr):
print("****************************Iteration: ", i,
"****************************")
print(trainer.train())
# "policy_03": (None, obs_space, act_space, {}),
# "policy_04": (None, obs_space, act_space, {}),
# },
# "policy_mapping_fn": policy_mapping_fn_eval
# },
# },
})
# Uncomment to restore from check-point
# ppo_trainer.restore('/home/ubuntu/ray_results/PPO_gfootball_2020-08-05_20-46-06e4wbrrqg/checkpoint_301/checkpoint-301')
for i in range(100000):
episode_data = []
print("+++++++++++++++Training iteration {!s}+++++++++++++++++++".format(i+1))
result = ppo_trainer.train()
print(pretty_print(result))
if ((i > 0) and (i % 10 == 0)):
print("===============Swapping weights===================")
P4key_P3val = {} # temp storage with "policy_4" keys & "policy_3" values
for (k4,v4), (k3,v3) in zip(ppo_trainer.get_policy("policy_04").get_weights().items(),
ppo_trainer.get_policy("policy_03").get_weights().items()):
P4key_P3val[k4] = v3
P3key_P2val = {} # temp storage with "policy_3" keys & "policy_2" values
for (k3,v3), (k2,v2) in zip(ppo_trainer.get_policy("policy_03").get_weights().items(),
ppo_trainer.get_policy("policy_02").get_weights().items()):
def _train(self):
ppo_trainer = PPOTrainer(env='c4', config=self.config)
while True:
result = ppo_trainer.train()
# reporter(**result)
print('ran iteration')
def my_train_fn(config, reporter):
active_policy = None
threshold = 0.7
trainer_updates = []
# ppo_trainer = MyPPOTrainer(env='c4', config=config)
ppo_trainer = PPOTrainer(env='c4', config=config)
bandit = Exp3Bandit(len(trainable_policies))
def func(worker):
worker.sampler.policy_mapping_fn = learned_vs_random_mapping_fn
foo = 1
# ppo_trainer.workers.foreach_worker(lambda w: w.sampler.policy_mapping_fn)
ppo_trainer.workers.foreach_worker(func)
# trainable_policies = ppo_trainer.workers.foreach_worker(lambda w: w.policies_to_train)[0][:]
# trainable_policies = ppo_trainer.workers.foreach_worker(
# lambda w: w.foreach_trainable_policy(lambda p, i: (i, p))
# )
while True:
result = ppo_trainer.train()
reporter(**result)
foo = 1
timestep = result['timesteps_total']
training_iteration = result['training_iteration']
# print('\n')
# print('$$$$$$$$$$$$$$$$$$$$$$$')
# print('timestep: {:,}'.format(timestep))
# print('trainable_policies: %s' % trainable_policies)
# if active_policy is None and timestep > int(5e6):
# # if active_policy is None and timestep > int(25e4):
# active_policy = trainable_policies[0]
# # ppo_trainer.workers.foreach_worker(
# # lambda w: w.foreach_trainable_policy(lambda p, i: (i, p))
# # )
# ppo_trainer.workers.foreach_worker(
# lambda w: w.policies_to_train.remove(trainable_policies[1])
# )
# trainer_updates.append(timestep)
# elif active_policy == trainable_policies[0] \
# and result['policy_reward_mean'][trainable_policies[0]] > threshold:
# active_policy = trainable_policies[1]
# ppo_trainer.workers.foreach_worker(
# lambda w: w.policies_to_train.remove(trainable_policies[0])
# )
# ppo_trainer.workers.foreach_worker(
# lambda w: w.policies_to_train.append(trainable_policies[1])
# )
# trainer_updates.append(timestep)
# elif active_policy == trainable_policies[1] \
# and result['policy_reward_mean'][trainable_policies[1]] > threshold:
# active_policy = trainable_policies[0]
# ppo_trainer.workers.foreach_worker(
# lambda w: w.policies_to_train.remove(trainable_policies[1])
# )
# ppo_trainer.workers.foreach_worker(
# lambda w: w.policies_to_train.append(trainable_policies[0])
# )
# trainer_updates.append(timestep)
#
# print('active_policy: %s' % active_policy)
# print('worker TPs: %s' % ppo_trainer.workers.foreach_worker(lambda w: w.policies_to_train)[0])
# print('trainer updates: %s' % '\n - ' + '\n - '.join('{:,}'.format(tu) for tu in trainer_updates))
# print('$$$$$$$$$$$$$$$$$$$$$$$')
# print('\n')
# if timestep > int(1e6):
# if training_iteration >= 20:
# break
# if result['episode_reward_mean'] > 200:
# phase = 2
# elif result['episode_reward_mean'] > 100:
# phase = 1
# else:
# phase = 0
# ppo_trainer.workers.foreach_worker(
# lambda ev: ev.foreach_env(
# lambda env: env.set_phase(phase)))
state = ppo_trainer.save()
ppo_trainer.stop()
num_policies = 4
policies = {
"policy_{}".format(i):
(None, env.observation_space, env.action_space, {})
for i in range(num_policies)
}
policy_ids = list(policies.keys())
config = {
"multiagent": {
"policies": policies,
"policy_mapping_fn": (lambda agent_id: random.choice(policy_ids)),
},
"framework": "tf",
"num_workers":
60 # Adjust this according to the number of CPUs on your machine.
}
trainer = PPOTrainer(env=TicTacToe, config=config)
best_eps_len = 0
mean_reward_thold = -1
while True:
results = trainer.train()
print(pretty_print(results))
if results["episode_reward_mean"] > mean_reward_thold and results[
"episode_len_mean"] > best_eps_len:
trainer.save("ttt_model")
best_eps_len = results["episode_len_mean"]
print("--------------------- MODEL SAVED!")
if results.get("timesteps_total") > 10**7:
break
ray.shutdown()
def main():
"""main function"""
ray.init()
if 'NUM_WORKERS' in os.environ:
num_of_workers = int(os.environ['NUM_WORKERS'])
else:
num_of_workers = DEFAULT_NUM_WORKERS
if os.path.isfile(WORLDS_JSON_PATH):
with open(WORLDS_JSON_PATH) as jsonfile:
dict_worlds = json.load(jsonfile)
else:
dict_worlds = None
if os.path.isfile(MASTER_URI_JSON_PATH):
with open(MASTER_URI_JSON_PATH) as jsonfile:
list_master_uri = json.load(jsonfile)['master_uri']
else:
list_master_uri = None
config = ppo.DEFAULT_CONFIG.copy()
config.update({
'env_config': {
'dict_worlds': dict_worlds,
'list_master_uri': list_master_uri, # 병렬 시뮬레이션 수행 스크립트 사용할 때
# 'list_master_uri': None, # 기본 ROS 마스터 URI로 시뮬레이션 1개만 돌릴 떄
'use_random_heading': True,
'result_csv': RESULT_CSV_NAME,
'num_workers': num_of_workers
},
'num_gpus': 0, # 사용하는 GPU 수에 맞게 설정
'num_workers': num_of_workers,
'train_batch_size': 10000,
'batch_mode': 'complete_episodes'
})
register_env('gazebo', lambda cfg: DroneSimEnv(cfg))
trainer = PPOTrainer(env='gazebo', config=config)
num_iteration = 10000
latest_index = 0
checkpoint_path = None
checkpoint_name = None
for name in [
name for name in os.listdir(CHECKPOINT_PATH_BASE)
if 'checkpoint_' in name
]:
index = int(name.replace('checkpoint_', ''))
if index > latest_index:
latest_index = index
checkpoint_path = CHECKPOINT_PATH_BASE + name + '/'
checkpoint_name = 'checkpoint-' + str(index)
if checkpoint_name:
print('Running using (', checkpoint_name, ').')
trainer.restore(checkpoint_path + checkpoint_name)
print(checkpoint_name, '==========================================')
## goal/collision data init
success_cnt = 0
goal_rate_filename = 'goal_rate_{}.csv'.format(
WORLDS_JSON_NAME.replace('curriculum/', '').replace('.json', ''))
if not os.path.isfile(goal_rate_filename):
with open(goal_rate_filename, 'w') as goal_rate_logfile:
goal_rate_logfile.write("training_iteration,goal_rate\n")
while True:
## goal/collision data create
with open(RESULT_CSV_NAME, 'w+') as file_:
pass
result = trainer.train()
print(pretty_print(result))
# 복구용 체크포인트는 5 iteration 마다 저장
if result['training_iteration'] % 5 == 0:
checkpoint = trainer.save(CHECKPOINT_PATH_BASE)
print("checkpoint saved at", checkpoint)
# 결과 확인용 체크포인트는 100 iteration 마다 저장
if result['training_iteration'] % 100 == 0:
checkpoint = trainer.save()
print("checkpoint saved at", checkpoint)
## goal/collision data read
with open(RESULT_CSV_NAME, 'r') as file_:
episodes_raw = file_.read()
goal_list = episodes_raw.split(',')
goal_cnt = goal_list.count('1')
if goal_cnt == 0:
goal_ratio = 0
else:
goal_ratio = goal_cnt / (goal_cnt + goal_list.count('0'))
print('goal rate:', goal_ratio)
with open(goal_rate_filename, 'a') as goal_rate_logfile:
goal_rate_logfile.write(
str(result['training_iteration']) + ',' + str(goal_ratio) +
'\n')
if goal_ratio >= 0.95:
success_cnt += 1
print('success in raw:', success_cnt)
else:
success_cnt = 0
if success_cnt >= 5 and EXIT_ON_SUCCESS:
if result['training_iteration'] % 5 != 0:
checkpoint = trainer.save(CHECKPOINT_PATH_BASE)
print("checkpoint saved at", checkpoint)
break
if result['training_iteration'] >= num_iteration:
break
print('PPO training is done.')