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Allow manual early stopping of training (Ctrl+C)

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Dominik Moritz Roth 2022-06-29 12:46:57 +02:00
parent e8d423f91f
commit 28561b9bb2
1 changed files with 21 additions and 49 deletions
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70
test.py
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@ -17,75 +17,39 @@ import columbus
root_path = '.' root_path = '.'
def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=500000, showRes=True, saveModel=True, n_eval_episodes=0): def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=200_000, showRes=True, saveModel=True, n_eval_episodes=0):
env = gym.make(env_name) env = gym.make(env_name)
test_sde = False use_sde = False
ppo = PPO( ppo = PPO(
"MlpPolicy", "MlpPolicy",
env, env,
verbose=0, verbose=0,
tensorboard_log=root_path+"/logs_tb/"+env_name+"/ppo/", tensorboard_log=root_path+"/logs_tb/" +
env_name+"/ppo"+(['', '_sde'][use_sde])+"/",
learning_rate=3e-4, learning_rate=3e-4,
gamma=0.99, gamma=0.99,
gae_lambda=0.95, gae_lambda=0.95,
normalize_advantage=True, normalize_advantage=True,
ent_coef=0.1, # 0.1 ent_coef=0.02, # 0.1
vf_coef=0.5, vf_coef=0.5,
use_sde=False, # False use_sde=use_sde, # False
clip_range=0.2,
) )
trl_pg = TRL_PG( trl_pg = TRL_PG(
"MlpPolicy", "MlpPolicy",
env, env,
verbose=0, verbose=0,
tensorboard_log=root_path+"/logs_tb/"+env_name+"/trl_pg/", tensorboard_log=root_path+"/logs_tb/"+env_name +
"/trl_pg"+(['', '_sde'][use_sde])+"/",
learning_rate=3e-4, learning_rate=3e-4,
gamma=0.99, gamma=0.99,
gae_lambda=0.95, gae_lambda=0.95,
normalize_advantage=True, normalize_advantage=True,
ent_coef=0.1, # 0.1 ent_coef=0.03, # 0.1
vf_coef=0.5, vf_coef=0.5,
use_sde=False, # False use_sde=use_sde,
clip_range=2, # 0.2
) )
if test_sde:
ppo_latent_sde = PPO(
"MlpPolicy",
env,
verbose=0,
tensorboard_log=root_path+"/logs_tb/"+env_name+"/ppo_latent_sde/",
learning_rate=3e-4,
gamma=0.99,
gae_lambda=0.95,
normalize_advantage=True,
ent_coef=0.15, # 0.1
vf_coef=0.5,
use_sde=True, # False
sde_sample_freq=30*15, # -1
)
trl_pg_latent_sde = TRL_PG(
"MlpPolicy",
env,
verbose=0,
tensorboard_log=root_path+"/logs_tb/"+env_name+"/trl_pg_latent_sde/",
learning_rate=3e-4,
gamma=0.99,
gae_lambda=0.95,
normalize_advantage=True,
ent_coef=0.15, # 0.1
vf_coef=0.5,
use_sde=True, # False
sde_sample_freq=30*15, # -1
)
# 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
# )
print('TRL_PG:') print('TRL_PG:')
testModel(trl_pg, timesteps, showRes, testModel(trl_pg, timesteps, showRes,
@ -97,13 +61,18 @@ def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=500000, showRes=True,
def testModel(model, timesteps, showRes=False, saveModel=False, n_eval_episodes=16): def testModel(model, timesteps, showRes=False, saveModel=False, n_eval_episodes=16):
env = model.get_env() env = model.get_env()
model.learn(timesteps) try:
model.learn(timesteps)
except KeyboardInterrupt:
print('[!] Training Terminated')
pass
if saveModel: if saveModel:
now = datetime.datetime.now().strftime('%d.%m.%Y-%H:%M') now = datetime.datetime.now().strftime('%d.%m.%Y-%H:%M')
loc = root_path+'/models/' + \ loc = root_path+'/models/' + \
model.tensorboard_log.replace( model.tensorboard_log.replace(
root_path+'/logs_tb/', '').replace('/', '_')+now+'.zip' root_path+'/logs_tb/', '').replace('/', '_')+now+'.zip'
print(model.get_parameters())
model.save(loc) model.save(loc)
if n_eval_episodes: if n_eval_episodes:
@ -132,3 +101,6 @@ def testModel(model, timesteps, showRes=False, saveModel=False, n_eval_episodes=
if __name__ == '__main__': if __name__ == '__main__':
main('LunarLanderContinuous-v2') main('LunarLanderContinuous-v2')
#main('ColumbusJustState-v0')
#main('ColumbusStateWithBarriers-v0')
#main('ColumbusEasierObstacles-v0')