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expanding automatic testing

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This commit is contained in:
Dominik Moritz Roth 2022-06-25 14:50:19 +02:00
parent 25316ec0b8
commit 1a49a412c0
1 changed files with 74 additions and 30 deletions
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100
test.py
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@ -16,21 +16,66 @@ import columbus
#root_path = os.getcwd()
root_path = '.'
def main(env_name='ColumbusStateWithBarriers-v0'):
def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=5000, showRes=False, saveModel=True, n_eval_episodes=8):
env = gym.make(env_name)
test_sde = False
ppo = PPO(
"MlpPolicy",
env,
verbose=0,
tensorboard_log=root_path+"/logs_tb/"+env_name+"/ppo/",
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=False, # False
)
trl_pg = TRL_PG(
"MlpPolicy",
env,
verbose=0,
tensorboard_log=root_path+"/logs_tb/"+env_name+"/trl_pg/",
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=False, # False
)
if test_sde:
ppo_latent_sde = PPO(
"MlpPolicy",
env,
verbose=0,
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
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(
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,
@ -40,34 +85,32 @@ def main(env_name='ColumbusStateWithBarriers-v0'):
# ent_coef=0.0016, #0.0032
# gamma=0.99, # 0.95
# learning_rate=0.001 # 0.015
#)
#trl = TRL_PG(
# "MlpPolicy",
# env,
# verbose=0,
# tensorboard_log="./logs_tb/"+env_name+"/trl_pg/",
#)
# )
print('PPO_LATENT_SDE:')
testModel(ppo_latent_sde, 500000, 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)
print('PPO:')
testModel(ppo, timesteps, showRes,
saveModel, n_eval_episodes)
print('TRL_PG:')
testModel(trl_pg, timesteps, showRes,
saveModel, n_eval_episodes)
def testModel(model, timesteps=150000, showRes=False, saveModel=False, n_eval_episodes=16):
def testModel(model, timesteps, showRes=False, saveModel=False, n_eval_episodes=16):
env = model.get_env()
model.learn(timesteps)
if saveModel:
now = datetime.datetime.now().strftime('%d.%m.%Y-%H:%M')
loc = root_path+'/models/'+model.tensorboard_log.replace(root_path+'/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)
print('Reward: '+str(round(mean_reward,3))+'±'+str(round(std_reward,2)))
mean_reward, std_reward = evaluate_policy(
model, env, n_eval_episodes=n_eval_episodes, deterministic=False)
print('Reward: '+str(round(mean_reward, 3)) +
'±'+str(round(std_reward, 2)))
if showRes:
input('<ready?>')
@ -75,7 +118,7 @@ def testModel(model, timesteps=150000, showRes=False, saveModel=False, n_eval_ep
# Evaluate the agent
episode_reward = 0
while True:
time.sleep(1/env.fps)
time.sleep(1/30)
action, _ = model.predict(obs, deterministic=False)
obs, reward, done, info = env.step(action)
env.render()
@ -86,5 +129,6 @@ def testModel(model, timesteps=150000, showRes=False, saveModel=False, n_eval_ep
obs = env.reset()
env.reset()
if __name__=='__main__':
main()
if __name__ == '__main__':
main('CartPole-v1')