dodox/metastable-baselines
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Testing the RayObserver

Browse Source
This commit is contained in:
Dominik Moritz Roth 2022-06-19 20:34:04 +02:00
parent 605a81c81c
commit 477a3c48b1
1 changed files with 38 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

59
test.py
View File

@ -7,54 +7,71 @@ from stable_baselines3 import SAC, PPO, A2C
from stable_baselines3.common.evaluation import evaluate_policy
from sb3_trl.trl_pg import TRL_PG
from subtrees.columbus import env
from columbus import env
register(
id='ColumbusTest3.1-v0',
entry_point=env.ColumbusTest3_1,
max_episode_steps=1000,
id='ColumbusTestRay-v0',
entry_point=env.ColumbusTestRay,
max_episode_steps=30*60*5,
)
def main():
#env = gym.make("LunarLander-v2")
env = gym.make("ColumbusTest3.1-v0")
env = gym.make("ColumbusTestRay-v0")
ppo = PPO(
"MlpPolicy",
env,
verbose=0,
tensorboard_log="./logs_tb/test/",
verbose=1,
tensorboard_log="./logs_tb/test/ppo",
use_sde=False,
ent_coef=0.0001,
learning_rate=0.0004
)
ppo_sde = PPO(
"MlpPolicy",
env,
verbose=1,
tensorboard_log="./logs_tb/test/ppo_sde/",
use_sde=True,
sde_sample_freq=30*20,
ent_coef=0.000001,
learning_rate=0.0003
)
a2c = A2C(
"MlpPolicy",
env,
verbose=0,
tensorboard_log="./logs_tb/test/",
verbose=1,
tensorboard_log="./logs_tb/test/a2c/",
)
trl = TRL_PG(
"MlpPolicy",
env,
verbose=0,
tensorboard_log="./logs_tb/test/",
tensorboard_log="./logs_tb/test/trl_pg/",
)
print('PPO:')
testModel(ppo)
print('A2C:')
testModel(a2c)
print('TRL_PG:')
testModel(trl)
#print('PPO:')
#testModel(ppo, 500000, showRes = True, saveModel=True, n_eval_episodes=4)
print('PPO_SDE:')
testModel(ppo_sde, 100000, showRes = True, saveModel=True, n_eval_episodes=0)
#print('A2C:')
#testModel(a2c, showRes = True)
#print('TRL_PG:')
#testModel(trl)
def testModel(model, timesteps=50000, showRes=False):
def testModel(model, timesteps=100000, showRes=False, saveModel=False, n_eval_episodes=16):
env = model.get_env()
model.learn(timesteps)
mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=16, deterministic=False)
print('Reward: '+str(round(mean_reward,3))+'±'+str(round(std_reward,2)))
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)))
if showRes:
model.save("model")
input('<ready?>')
obs = env.reset()
# Evaluate the agent
episode_reward = 0