def play(env_name, seed, load_file, total_timesteps, n_cpu):
np.set_printoptions(precision=5)
def padding_obss(obss, dummy_obss):
dummy_obss[ 0, :, :, : ] = obss
return dummy_obss
# trained LSTM model cannot change number of env.
# so it needs to reshape observation by padding dummy data.
dummy_obss = np.zeros((n_cpu, 64, 64, 4))
env = SubprocVecEnv([make_env(env_name, 0, seed)])
model = PPO2.load(load_file, verbose=1)
obss = env.reset()
obss = padding_obss(obss, dummy_obss)
rewards_buf = []
steps_buf = []
# TODO: single
for i in range(total_timesteps):
actions, _states = model.predict(obss)
actions = actions[0:1]
obss, rewards, dones, infos = env.step(actions)
obss = padding_obss(obss, dummy_obss)
# env.render() # dummy
if dones[0]:
rewards_buf.append(infos[0]['episode']['r'])
steps_buf.append(infos[0]['episode']['l'])
line = np.array([np.mean(rewards_buf), np.std(rewards_buf), np.mean(steps_buf), np.std(steps_buf)])
print(len(rewards_buf), line)
obss = env.reset()
obss = padding_obss(obss, dummy_obss)
env.close()
def main():
#env = SubprocVecEnv([(lambda i=i: SwocGym(i+1, GameServicePath, i, fieldWidth=10, fieldHeight=10, saveEpisode=True)) for i in range(1)])
env = SubprocVecEnv([
(lambda i=i: MazeGym(mazeWidth=10, mazeHeight=10, nrWallsToRemove=10))
for i in range(1)
])
try:
model = PPO2("MlpPolicy",
env,
verbose=1,
tensorboard_log='/home/ralph/swoc2019/log')
if SaveFile.exists():
print('loading...')
model.load_parameters(SaveFile)
else:
print('Warning: No save file loaded')
print('evaluating...', end='')
obs = env.reset()
totalRewards = None
for i in range(100):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
totalRewards = totalRewards + rewards if totalRewards is not None else rewards
env.render()
sleep(0.2)
print(f'mean reward: {np.mean(totalRewards)}')
except KeyboardInterrupt:
print('closing...')
finally:
env.close()
print('closed')
def train():
def callback(_locals, _globals):
# Save model
_locals['self'].save(MODEL_NAME)
envs = [create_env_headless for _ in range(ENV_COUNT)]
vec_envs = SubprocVecEnv(envs)
model = PPO2('CnnPolicy',
vec_envs,
verbose=1,
ent_coef=0.0001,
n_steps=256)
if not os.path.isfile(MODEL_NAME):
model.save(MODEL_NAME)
vec_envs.close()
print("Run again to train")
else:
model.learn(total_timesteps=TIMESTEPS, callback=callback)
model.save(MODEL_NAME)
vec_envs.close()
print("Training Done")
# Evaluation
print("Evaluation")
vec_env = create_env_headless()
vec_env = DummyVecEnv([lambda: vec_env])
model = PPO2.load(MODEL_NAME)
print(evaluate_policy(model, vec_env, n_eval_episodes=100))
print(evaluate_policy(model, vec_env, n_eval_episodes=100))
vec_env.close()
def train_policy():
ppo_config = {
"gamma": 0.9988,
"n_steps": 200,
"ent_coef": 0,
"learning_rate": 0.001,
"vf_coef": 0.99,
"max_grad_norm": 0.1,
"lam": 0.95,
"nminibatches": 5,
"noptepochs": 100,
"cliprange": 0.2,
"tensorboard_log": log_relative_path
}
os.makedirs(log_relative_path)
policy_kwargs = dict(act_fun=tf.nn.tanh, net_arch=[256, 128])
env = SubprocVecEnv([_make_env(rank=i) for i in range(5)])
model = PPO2(MlpPolicy,
env,
_init_setup_model=True,
policy_kwargs=policy_kwargs,
verbose=1,
**ppo_config)
model.learn(total_timesteps=1000,
tb_log_name="ppo2",
reset_num_timesteps=False)
model.save(os.path.join(log_relative_path, 'model'))
env.env_method("save_world", log_relative_path)
env.close()
return
def main():
env = SubprocVecEnv([(lambda i=i: SwocGym(
i + 1, GameServicePath, i, actionRepeat=4, oneTarget=True))
for i in range(4)])
try:
model = PPO2("MlpPolicy",
env,
verbose=1,
policy_kwargs={
'net_arch': [256, 256, 256, 128, 128, 128],
'act_fun': tf.nn.relu
},
n_steps=256,
ent_coef=0.0,
learning_rate=1e-5)
if SaveFile.exists():
print('loading...')
model.load_parameters(SaveFile)
else:
print('Warning: No save file loaded')
print('evaluating...', end='')
totalRewards = evaluate(env, model)
print(f'mean reward: {np.mean(totalRewards)}')
except KeyboardInterrupt:
print('closing...')
finally:
env.close()
print('closed')
def play(env_name, seed, load_file, total_timesteps, n_cpu):
np.set_printoptions(precision=5)
def padding_obss(obss, dummy_obss):
dummy_obss[ 0, :, :, : ] = obss
return dummy_obss
# if it's GUI mode, number of env is changed to 1 to reduce GUI windows.
# but trained LSTM model cannot change number of env.
# so it needs to reshape observation by padding dummy data.
isGUI = env_name.find('GUI') != -1
dummy_obss = np.zeros((n_cpu, 64, 64, 4)) if isGUI else None
env = SubprocVecEnv([make_env(env_name, i, seed) for i in range(1 if isGUI else n_cpu)])
model = PPO2.load(load_file, verbose=1)
obss = env.reset()
obss = padding_obss(obss, dummy_obss) if isGUI else obss
rewards_buf = []
steps_buf = []
# TODO: single
for i in range(total_timesteps):
actions, _states = model.predict(obss)
actions = actions[0:1] if isGUI else actions
obss, rewards, dones, infos = env.step(actions)
obss = padding_obss(obss, dummy_obss) if isGUI else obss
# env.render() # dummy
if dones.any():
rewards_buf.extend([ info['episode']['r'] for info in infos if 'episode' in info ])
steps_buf.extend([ info['episode']['l'] for info in infos if 'episode' in info ])
line = np.array([np.mean(rewards_buf), np.std(rewards_buf), np.mean(steps_buf), np.std(steps_buf)])
print(len(rewards_buf), line)
env.close()
def run_experiment(args):
hyperparam_file = os.path.join(HYPERPARAM_DIR, args.agent + ".yml")
hyperparams = yaml.safe_load(open(hyperparam_file))
hyperparams = hyperparams[args.env]
n_envs = hyperparams.pop("n_envs", 1)
n_timesteps = int(hyperparams.pop("n_timesteps"))
policy = hyperparams.pop("policy")
normalize = hyperparams.pop("normalize", None)
vecEnv = []
for i in range(n_envs):
# Bit of trickery here to avoid referencing
# to the same "i"
vecEnv.append((lambda idx: lambda: create_env(args, idx))(i))
if args.subprocenv:
vecEnv = SubprocVecEnv(vecEnv)
else:
vecEnv = DummyVecEnv(vecEnv)
# Handle learning rates
# Taken from rl-zoo/train.py
for key in ['learning_rate', 'cliprange', 'cliprange_vf']:
if key not in hyperparams or args.agent == "dqn":
continue
if key == 'learning_rate' and args.agent == "a2c":
continue
if isinstance(hyperparams[key], str):
schedule, initial_value = hyperparams[key].split('_')
initial_value = float(initial_value)
hyperparams[key] = linear_schedule(initial_value)
elif isinstance(hyperparams[key], (float, int)):
# Negative value: ignore (ex: for clipping)
if hyperparams[key] < 0:
continue
hyperparams[key] = constfn(float(hyperparams[key]))
if args.forced_cliprange is not None:
hyperparams["cliprange"] = args.forced_cliprange
agent_class = AVAILABLE_ALGORITHMS[args.agent]
agent = agent_class(policy, vecEnv, verbose=1, **hyperparams)
# Prepare callback
checkpoint_dir = os.path.join(args.output, CHECKPOINT_DIR)
os.makedirs(checkpoint_dir)
# Note that save_freq is counted in number of agent step-calls,
# not env step-calls.
save_freq = n_timesteps // (args.num_snapshots * n_envs)
checkpoint_callback = CheckpointCallback(save_freq, checkpoint_dir)
agent.learn(total_timesteps=n_timesteps, callback=checkpoint_callback)
vecEnv.close()
def train():
n_cpu = os.cpu_count()
env = SubprocVecEnv([lambda: DemoEnv() for i in range(n_cpu)])
model = PPO2(MlpPolicy,
env,
verbose=1,
policy_kwargs={'net_arch': [dict(vf=[4], pi=[4])]})
model.learn(total_timesteps=int(1e6))
model.save("ppo2_DemoEnv")
env.close()
del model
def run_training(config: Dict):
"""Runs training based on config passed in"""
print("Run configuration:")
print(config)
seed(config['seed'])
# read config
hyperparameters = read_hyperparameters(config)
graphs = graphs_from_args(config['graphs'])
policy, policy_kwargs = policy_from_args(config, graphs)
demands = demands_from_args(config, graphs)
env_kwargs = env_kwargs_from_args(config)
env_name = config['env_name']
timesteps = config['timesteps']
parallelism = config['parallelism']
log_name = config['log_name']
model_name = config['model_name']
tensorboard_log = config['tensorboard_log']
oblivious_routings = None
# make env
env = lambda: gym.make(env_name,
dm_sequence=demands,
graphs=graphs,
oblivious_routings=oblivious_routings,
**env_kwargs)
vec_env = SubprocVecEnv([env for _ in range(parallelism)],
start_method="spawn")
# make model
model = PPO2(policy,
vec_env,
cliprange_vf=-1,
verbose=1,
policy_kwargs=policy_kwargs,
tensorboard_log=tensorboard_log,
**hyperparameters)
# learn
if env_name == 'ddr-iterative-v0':
model.learn(total_timesteps=timesteps, tb_log_name=log_name)
else:
model.learn(total_timesteps=timesteps, tb_log_name=log_name)
# save it
model.save(model_name)
# make sure everything stopped correctly
vec_env.close()
def get_rewards(self,
skills=[],
train_total_timesteps=5000000,
eval_times=100,
eval_max_steps=10000,
model_save_name=None,
add_info={}):
# def get_rewards(self, skills=[], train_total_timesteps=10, eval_times=10, eval_max_steps=10, model_save_name=None, add_info={}):
"""
:param skills: (list) the availiable action sequence for agent
e.g [[0,2,2],[0,1,1]]
:param train_total_timesteps: (int)total_timesteps to train
:param eval_times: (int)the evaluation times
e.g eval_times=100, evalulate the policy by averageing the reward of 100 episode
:param eval_max_steps: (int)maximum timesteps per episode when evaluate
:param model_save_name: (str)specify the name of saved model (should not repeat)
:param add_info: (dict) other information to log in log.txt
"""
# env = SkillWrapper(self.env, skills=skills)
if self.num_cpu > 1:
env = SubprocVecEnv([
self.make_env(self.env_creator, i, skills)
for i in range(self.num_cpu)
])
else:
env = DummyVecEnv([lambda: self.env_creator()])
model = self.model(self.policy, env, verbose=self.verbose)
self.strat_time = time.time()
print("start to train agent...")
model.learn(total_timesteps=train_total_timesteps,
reset_num_timesteps=self.reset_num_timesteps)
print("Finish train agent")
if self.save_path is not None:
if self.preserve_model > 0:
self.save_model(model, model_save_name, skills=skills)
# evaluate
info = self.evaluate(env, model, eval_times, eval_max_steps)
env.close()
#log result
info.update(add_info)
self.log(info)
self._serial_num = self._serial_num + 1
return info["ave_score"], info["ave_action_reward"]
class SimulatorModel(object):
def __init__(self, _make_env_func, parallel_agents):
"""
This class instantiates a dynamics model based on the pybullet simulator
(i.e: simulates exactly the result of the actions), it can be used
for reward tuning and verifying tasks..etc
:param _make_env_func: (func) a function if called it will return a gym
environment.
:param parallel_agents: (int) number of parallel agents to siumulate
to evaluate the actions.
"""
self.parallel_agents = parallel_agents
self.envs = SubprocVecEnv(
[_make_env_func() for i in range(self.parallel_agents)])
return
def evaluate_trajectories(self, action_sequences):
"""
A function to be called to evaluate the action sequences and return
the corresponding reward for each sequence.
:param action_sequences: (nd.array) actions to be evaluated
(number of sequences, horizon length)
:return: (nd.array) sum of rewards for each action sequence.
"""
horizon_length = action_sequences.shape[1]
num_of_particles = action_sequences.shape[0]
rewards = np.zeros([num_of_particles])
assert ((float(num_of_particles) / self.parallel_agents).is_integer())
for j in range(0, num_of_particles, self.parallel_agents):
self.envs.reset()
total_reward = np.zeros([self.parallel_agents])
for k in range(horizon_length):
actions = action_sequences[j:j + self.parallel_agents, k]
task_observations, current_reward, done, info = \
self.envs.step(actions)
total_reward += current_reward
rewards[j:j + self.parallel_agents] = total_reward
return rewards
def end_sim(self):
"""
Closes the environments that were used for simulation.
:return:
"""
self.envs.close()
return
def train():
def callback(_locals, _globals):
global n_steps
if (n_steps + 1) % 100 == 0:
_locals['self'].save(MODEL_NAME)
n_steps += 1
envs = [create_env_headless_monitor for _ in range(ENV_COUNT)]
envs = SubprocVecEnv(envs)
model = PPO2('CnnPolicy', envs, verbose=1, ent_coef=0.0001, n_steps=512)
model.save(MODEL_NAME)
model.learn(total_timesteps=TIMESTEPS, callback=callback)
model.save(MODEL_NAME)
print("Training Done")
envs.close()
def _eval_model(model, env_id, ob_shape, num_eps, plot=False):
test_env = SubprocVecEnv([make_env(env_id)])
sharpe_ratios = []
for episode in range(num_eps):
# Padding zeros to the test env to match the shape of the training env.
zero_completed_obs = np.zeros((NUM_CPU,) + ob_shape)
zero_completed_obs[0, :] = test_env.reset()
state = None
for _ in range(L):
action, state = model.predict(zero_completed_obs, state=state, deterministic=True)
zero_completed_obs[0, :], reward, done, _ = test_env.env_method('step', action[0], indices=0)[0]
sharpe_ratios.append(test_env.env_method('get_sharpe_ratio', indices=0)[0])
if plot: test_env.env_method('render', indices=0)
test_env.close()
# Return the average sharpe ratio
return sum(sharpe_ratios) / len(sharpe_ratios)
def test():
# Parallel environments
n_cpu = 4
env = SubprocVecEnv([lambda: RSEnv() for i in range(n_cpu)])
model = A2C(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=600000, log_interval=10)
model.save("sba2c")
env = TestRSEnv()
obs = env.reset()
done = False
while not done:
action, _ = model.predict(obs)
obs, rewards, done, info = env.step(action)
env.render()
env.close()
def main():
# env = SubprocVecEnv([(lambda i=i: SwocGym(i+1, GameServicePath, i, fieldWidth=10, fieldHeight=10)) for i in range(16)])
env = SubprocVecEnv([
(lambda i=i: MazeGym(mazeWidth=10, mazeHeight=10, nrWallsToRemove=60))
for i in range(12)
])
try:
#model = PPO2("MlpPolicy", env, verbose=1, ent_coef=0.01, tensorboard_log='/home/ralph/swoc2019/log')
#model = PPO2("MlpPolicy", env, verbose=1, policy_kwargs={'net_arch': [1024,1024,512,512,256,256,128,128,64,64], 'act_fun': tf.nn.relu},
# n_steps=64, ent_coef=0.01, learning_rate=1e-5, tensorboard_log='/home/ralph/swoc2019/log')
model = PPO2("MlpPolicy",
env,
verbose=1,
policy_kwargs={
'net_arch':
[1024, 1024, 512, 512, 256, 256, 128, 128, 64, 64],
'act_fun':
tf.nn.relu
})
if SaveFile.exists():
print('loading...', end='')
model.load_parameters(SaveFile)
print('loaded!')
else:
# No weights loaded, so remove history
with open(RewardsLog, 'w+') as file:
file.write('')
try:
print('learning...')
model.learn(total_timesteps=100000000, callback=callback)
finally:
print('saving...', end='')
model.save(SaveFile)
print('saved!')
except KeyboardInterrupt:
print('closing...', end='')
finally:
env.close()
print('closed!')
def learn(env_name, seed, load_file, save_file, tensorboard_log, total_timesteps, n_cpu):
best_mean_reward = -np.inf
best_mean_step = np.inf
save_file = env_name if save_file is None else save_file
best_save_file = save_file + BEST_SAVE_FILE_SUFFIX
start_time = time.time()
def callback(_locals, _globals):
nonlocal best_mean_reward, best_mean_step
t = (time.time() - start_time) / 3600.0
print(f'hours: {t:.2f}')
ep_info_buf = _locals['ep_info_buf']
if len(ep_info_buf) < ep_info_buf.maxlen:
return True
mean_reward = np.mean([ ep_info['r'] for ep_info in ep_info_buf ])
mean_step = np.mean([ ep_info['l'] for ep_info in ep_info_buf ])
if mean_reward > best_mean_reward:
best_mean_reward = mean_reward
print('best_mean_reward:', best_mean_reward)
print('saving new best model:', best_save_file)
_locals['self'].save(best_save_file)
if mean_step < best_mean_step:
best_mean_step = mean_step
print('best_mean_step:', best_mean_step)
return True # False should finish learning
# policy = CnnPolicy
policy = CnnLstmPolicy
# policy = CnnLnLstmPolicy
print(env_name, policy)
# Run this to enable SubprocVecEnv on Mac OS X.
# export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
# see https://github.com/rtomayko/shotgun/issues/69#issuecomment-338401331
env = SubprocVecEnv([make_env(env_name, i, seed) for i in range(n_cpu)])
if load_file is not None:
model = PPO2.load(load_file, env, verbose=1, tensorboard_log=tensorboard_log)
else:
model = PPO2(policy, env, verbose=1, tensorboard_log=tensorboard_log)
model.learn(total_timesteps=total_timesteps, log_interval=5, callback=callback)
print('saving model:', save_file)
model.save(save_file)
env.close()
def learn(env_name, seed, load_path, save_path, tensorboard_log, total_timesteps, n_cpu):
save_path = env_name if save_path is None else save_path
checkpoint_callback = CheckpointCallback(save_freq=2000, save_path=save_path)
eval_env = make_env(env_name, n_cpu, seed)()
eval_callback = EvalCallback(eval_env, best_model_save_path=save_path+'/best', log_path=tensorboard_log, eval_freq=1000)
callback = CallbackList([checkpoint_callback, eval_callback])
policy = CnnPolicy
# policy = CnnLstmPolicy
# policy = CnnLnLstmPolicy
print(env_name, policy)
# Run this to enable SubprocVecEnv on Mac OS X.
# export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
# see https://github.com/rtomayko/shotgun/issues/69#issuecomment-338401331
env = SubprocVecEnv([make_env(env_name, i, seed) for i in range(n_cpu)])
if load_path is not None:
model = PPO2.load(load_path, env, verbose=1, tensorboard_log=tensorboard_log)
else:
model = PPO2(policy, env, verbose=1, tensorboard_log=tensorboard_log)
model.learn(total_timesteps=total_timesteps, log_interval=5, callback=callback)
print('saving model:', save_path+'/latest_model')
model.save(save_path+'/latest_model')
env.close()
def main():
env = SubprocVecEnv([(lambda i=i: SwocGym(
i + 1, GameServicePath, i, actionRepeat=4, oneTarget=True))
for i in range(16)])
try:
model = PPO2("MlpPolicy",
env,
verbose=1,
policy_kwargs={
'net_arch': [256, 256, 256, 128, 128, 128],
'act_fun': tf.nn.relu
},
n_steps=32,
ent_coef=0.1,
learning_rate=1e-4,
tensorboard_log='/home/ralph/swoc2019/log')
if SaveFile.exists():
print('loading...')
model.load_parameters(SaveFile)
else:
# No weights loaded, so remove history
with open(RewardsLog, 'w+') as file:
file.write('')
try:
print('learning...')
model.learn(total_timesteps=100000000, callback=callback)
finally:
print('saving...')
model.save(SaveFile)
print('saved!')
except KeyboardInterrupt:
print('closing...')
finally:
env.close()
print('closed')
def train(exp_name, env_name, n_envs, **kwargs):
# Train 10 runs
for n in range(1, 11): # PPO2_n
# Configure logger
log_folder = 'training_logs/' + exp_name + '_' + str(n) + '/'
logger.configure(log_folder, ['csv'])
print("[+] Starting training", n)
env = SubprocVecEnv([
make_env(log_folder, env_name, i, (n - 1) * 32)
for i in range(n_envs)
])
model = PPO2(
policy=MlpPolicy,
env=env,
verbose=True,
# Make it deterministic
seed=32 * n, # Fixed seed
n_cpu_tf_sess=1, # force deterministic results
# Pass arguments
**kwargs)
model.learn(
total_timesteps=int(250e3),
log_interval=1, # log each update
)
# Saving model
os.makedirs("trained_models", exist_ok=True)
model.save("trained_models/" + exp_name + "_" + str(n))
env.close()
del env
del model
env = SubprocVecEnv([make_env(env_id, params) for _ in range(4)],
start_method='fork')
policy_kwargs = dict()
model = A2C('MlpLstmPolicy',
env,
learning_rate=1e-3,
verbose=1,
n_steps=64,
tensorboard_log="/tmp",
gamma=0.99,
policy_kwargs=policy_kwargs)
model.learn(total_timesteps=int(params["steps"]))
print("Done learning, saving model")
model.save("agents/SBL_{}".format(params["ID"]))
print("Saved model, closing env")
env.close()
print("Finished training with ID: {}".format(ID))
else:
#env_vec = SubprocVecEnv([make_env(env_id, params) for _ in range(4)], start_method='fork')
env = env_id(params["env_list"],
max_n_envs=1,
specific_env_len=70,
s_len=150,
walls=True,
target_vel=params["target_vel"],
use_contacts=params["use_contacts"])
print("Testing")
policy_name = "QWZ" # LX3: joints + contacts + yaw
policy_path = 'agents/SBL_{}'.format(policy_name)
model = A2C.load(policy_path)
def main():
args = get_args()
log_dir = create_log_dir(args)
if not args.test:
writer = SummaryWriter(log_dir)
else:
writer = None
SEED = 721
if args.ram_obs or args.env == "slimevolley_v0":
obs_type = 'ram'
else:
obs_type = 'rgb_image'
env = make_env(
args.env, SEED, obs_type=obs_type
) # TODO used for providing spaces info, can also modify SubprocVecEnv wrapper
# https://stable-baselines.readthedocs.io/en/master/guide/vec_envs.html?highlight=multiprocessing
envs = SubprocVecEnv([
lambda: make_env(args.env, obs_type=obs_type)
for _ in range(args.num_envs)
],
start_method='spawn')
# envs.seed(np.random.randint(1000, size=args.num_envs).tolist()) # random seeding
envs.seed(SEED) # fix seeding
state_spaces = env.observation_spaces
action_spaces = env.action_spaces
print('state_spaces: ', state_spaces, ', action_spaces: ', action_spaces)
learner_args = {'device': args.device}
env.reset()
agents = env.agents
print('agents: ', agents)
if args.train_both:
fixed_agents = []
else:
fixed_agents = [
'first_0'
] # SlimeVolley: opponent is the first, the second agent is the learnable one
if obs_type == 'ram':
model = ParallelMultiPPODiscrete(args.num_envs, agents, state_spaces,
action_spaces, 'MLP', fixed_agents,
learner_args,
**hyperparams).to(args.device)
else:
model = ParallelMultiPPODiscrete(args.num_envs, agents, state_spaces,
action_spaces, 'CNN', fixed_agents,
learner_args,
**hyperparams).to(args.device)
load_model(model, args)
path = f"model/{args.env}/"
os.makedirs(path, exist_ok=True)
if args.fictitious:
path = path + 'fictitious_'
parallel_rollout(envs, model, writer, max_eps=max_eps, max_timesteps=max_timesteps, selfplay_interval=selfplay_interval,\
render=args.render, model_path=path, against_baseline=args.against_baseline, selfplay=args.selfplay, \
fictitious=args.fictitious, test=args.test)
envs.close()
def main():
args = get_configuration()
args.state_dim = util.get_state_dim(args)
if not os.path.exists(args.outdir):
os.makedirs(args.outdir, exist_ok=True)
if args.graph_embedding:
class MyPolicy(EmbeddingPolicy):
def __init__(self,
sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=True,
**_kwargs):
super().__init__(sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
args,
reuse=reuse,
**_kwargs)
else:
class MyPolicy(EnigmaPolicy):
def __init__(self,
sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=True,
**_kwargs):
super().__init__(sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
args,
reuse=reuse,
**_kwargs)
t0 = time.time()
from mpi4py import MPI as mpi
comm = mpi.COMM_WORLD
rank = comm.Get_rank()
all = comm.Get_size()
gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(',')
gpu_count = len(gpus)
gpu = gpus[rank % gpu_count]
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
print("My rank is {} out of {}, using GPU {}".format(rank, all, gpu))
if args.model_type == "ppo2":
from stable_baselines import PPO2 as PPO
env = SubprocVecEnv([(lambda: ProofEnv.ProofEnv(args))
for _ in range(args.parallel_envs)
]) #, start_method="spawn")
elif args.model_type == "ppo1":
args.parallel_envs = 1
env = DummyVecEnv([lambda: ProofEnv.ProofEnv(args)])
# from stable_baselines import PPO1 as PPO
from ppo import PPO1 as PPO
if args.saved_model == None:
myPolicy = MyPolicy
if args.model_type == "ppo2":
model = PPO(
policy=myPolicy,
env=env,
n_steps=args.actorbatch,
# nminibatches=args.optim_stepsize,
lam=0.95,
gamma=args.gamma,
noptepochs=4,
ent_coef=args.entcoeff,
learning_rate=lambda f: f * 2.5e-4,
cliprange=lambda f: f * 0.1,
verbose=1)
elif args.model_type == "ppo1":
model = PPO(myPolicy,
env,
verbose=2,
timesteps_per_actorbatch=args.actorbatch,
schedule=args.lr_schedule,
optim_stepsize=args.optim_stepsize,
entcoeff=args.entcoeff,
optim_batchsize=args.optim_batchsize,
gamma=args.gamma)
else:
print("Loading model from {}".format(args.saved_model))
model = PPO.load(args.saved_model)
model.set_env(env)
counter = 0
for ind in range(args.parallel_envs):
env.env_method("set_model",
model,
indices=list(range(args.parallel_envs)))
modelfiles = []
for train_timestep, train_dir in zip(args.train_timesteps,
args.train_dirs):
problem_files = sorted(util.list_problems(train_dir))
problem_files = util.split_list(problem_files, all)[rank]
problem_files_splitted = util.split_list(problem_files,
args.parallel_envs,
extensible=False)
if args.add_repeating_pretraining:
for ind in range(args.parallel_envs):
env.env_method("set_source",
problem_files_splitted[ind],
indices=[ind],
generator_type="repeating")
# all_thread_timestep = train_timestep * all
print("PRETRAINING")
model.learn(total_timesteps=train_timestep)
print("Pretraining on {} finished in {}".format(
train_dir, util.format_time(time.time() - t0)))
for ind in range(args.parallel_envs):
env.env_method("set_source",
problem_files_splitted[ind],
indices=[ind])
# all_thread_timestep = train_timestep * all
model.learn(total_timesteps=train_timestep)
modelfile = "{}/ppo1_fcop_train_{}".format(args.outdir, counter)
modelfiles.append(modelfile)
if rank == 0:
model.save(modelfile)
# logger.logkv("finished_train_problems", counter)
counter += 1
print("Training on {} finished in {}".format(
train_dir, util.format_time(time.time() - t0)))
statistics_list = env.get_attr("statistics",
indices=list(range(args.parallel_envs)))
blacklist_list = env.get_attr("blacklist",
indices=list(range(args.parallel_envs)))
for i, statistics in enumerate(statistics_list):
print("ENV {} - {} - blacklist: {}\n".format(
rank, i, blacklist_list[i])),
util.print_problemdict(statistics, rank)
# for f in statistics:
# statistics[f]["mcts"].display_tree([0])
# util.print_problemdict(env.envs[0].statistics)
if len(args.train_dirs) > 0 and len(
args.train_timesteps) > 0: # we did training
print("We have finished training, rank {}".format(rank))
# for p in problem_files:
# vis_policy.vis_policy(env.envs[0], model, p)
env.close()
del env
del model
# here we wait for everyone
comm.Barrier()
print("We have started evaluation, rank {}".format(rank))
# evaluation without training
if (args.saved_model is not None) and (len(
args.train_dirs) == 0): # no training, just evaluation
modelfiles = [args.saved_model]
for evaldir in args.evaldirs:
for model_index, modelfile in enumerate(modelfiles):
eval.eval_mpi(args, evaldir, modelfile, model_index)
# here we wait for everyone
comm.Barrier()
callbacks += 1
if RENDER_TO_SCREEN:
locals["self"].env.render()
# Saves the model every 1000 calls
if callbacks % 10000 == 0:
locals['self'].save("models/" + folderName + "/" + runName + "-" +
str(callbacks))
return True # Returns true as false ends the training
n = 6
list = [lambda: gym.make('gvgai-aliens-lvl0-v0') for _ in range(n)] + \
[lambda: gym.make('gvgai-aliens-lvl1-v0') for _ in range(n)] + \
[lambda: gym.make('gvgai-boulderdash-lvl0-v0') for _ in range(n)] + \
[lambda: gym.make('gvgai-boulderdash-lvl1-v0') for _ in range(n)] + \
[lambda: gym.make('gvgai-missilecommand-lvl0-v0') for _ in range(n)] + \
[lambda: gym.make('gvgai-missilecommand-lvl1-v0') for _ in range(n)]
# multiprocess environment
n_cpu = multiprocessing.cpu_count()
venv = SubprocVecEnv(list)
venv = EnvWrapper(venv, (128, 128, 3)) #(110, 300, 3)
model = A2C(ONet,
venv,
verbose=1,
tensorboard_log="tensorboard/" + folderName + "/",
n_steps=stepsUpdate)
model.learn(total_timesteps=int(1e8), tb_log_name=runName, callback=callback)
venv.close()
model.save("models/" + folderName + "/" + runName + "-Final")