Fixed model storage location bug

This commit is contained in:
Dominik Moritz Roth 2022-06-22 13:00:40 +02:00
parent 41d4e94dbe
commit 0e17b4c07e

51
test.py Normal file → Executable file
View File

@ -1,6 +1,8 @@
#!/bin/python3
import gym
from gym.envs.registration import register
import numpy as np
import os
import time
import datetime
@ -11,32 +13,34 @@ from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticP
from sb3_trl.trl_pg import TRL_PG
import columbus
#root_path = os.getcwd()
root_path = '.'
def main(env_name='ColumbusEasierObstacles-v0'):
def main(env_name='ColumbusStateWithBarriers-v0'):
env = gym.make(env_name)
ppo_latent_sde = PPO(
"MlpPolicy",
env,
verbose=0,
tensorboard_log="./logs_tb/"+env_name+"/ppo_latent_sde/",
use_sde=True,
tensorboard_log=root_path+"/logs_tb/"+env_name+"/ppo_latent_sde/",
#use_sde=True,
sde_sample_freq=30*15,
ent_coef=0.0016/1.25, #0.0032
vf_coef=0.00025/2, #0.0005
gamma=0.99, # 0.95
learning_rate=0.005/5 # 0.015
)
sac_latent_sde = SAC(
"MlpPolicy",
env,
verbose=0,
tensorboard_log="./logs_tb/"+env_name+"/sac_latent_sde/",
use_sde=True,
sde_sample_freq=30*15,
ent_coef=0.0016, #0.0032
gamma=0.99, # 0.95
learning_rate=0.001 # 0.015
#ent_coef=0.0016/1.25, #0.0032
#vf_coef=0.00025/2, #0.0005
#gamma=0.99, # 0.95
#learning_rate=0.005/5 # 0.015
)
#sac_latent_sde = SAC(
# "MlpPolicy",
# env,
# verbose=0,
# tensorboard_log=root_path+"/logs_tb/"+env_name+"/sac_latent_sde/",
# use_sde=True,
# sde_sample_freq=30*15,
# ent_coef=0.0016, #0.0032
# gamma=0.99, # 0.95
# learning_rate=0.001 # 0.015
#)
#trl = TRL_PG(
# "MlpPolicy",
# env,
@ -44,10 +48,10 @@ def main(env_name='ColumbusEasierObstacles-v0'):
# tensorboard_log="./logs_tb/"+env_name+"/trl_pg/",
#)
#print('PPO_LATENT_SDE:')
#testModel(ppo_latent_sde, 1000000, showRes = True, saveModel=True, n_eval_episodes=3)
print('SAC_LATENT_SDE:')
testModel(ppo_latent_sde, 250000, showRes = True, saveModel=True, n_eval_episodes=0)
print('PPO_LATENT_SDE:')
testModel(ppo_latent_sde, 25000, showRes = True, saveModel=True, n_eval_episodes=3)
#print('SAC_LATENT_SDE:')
#testModel(sac_latent_sde, 250000, showRes = True, saveModel=True, n_eval_episodes=0)
#print('TRL_PG:')
#testModel(trl)
@ -58,7 +62,8 @@ def testModel(model, timesteps=150000, showRes=False, saveModel=False, n_eval_ep
if saveModel:
now = datetime.datetime.now().strftime('%d.%m.%Y-%H:%M')
model.save('models/'+model.tensorboard_log.replace('./logs_tb/','').replace('/','_')+now+'.zip')
loc = root_path+'/models/'+model.tensorboard_log.replace(root_path+'/logs_tb/','').replace('/','_')+now+'.zip'
model.save(loc)
if n_eval_episodes:
mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=n_eval_episodes, deterministic=False)