import __future__
import gym
from stable_baselines.common.vec_env import VecCheckNan
from stable_baselines.common import make_vec_env
from stable_baselines import PPO2
env = make_vec_env('CMuRL_Env:CMuRL-Env-v0')
env = VecCheckNan(env, raise_exception=True)
model = PPO2.load('CMuRL_Model_v6')
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, _, _ = env.step(action)
env.render()
gym.register(id="rustyblocks-v0", entry_point="custom_env:RustyBlocksEnv")
env = gym.make("rustyblocks-v0")
env.max_invalid_tries = 7
env = VecCheckNan(DummyVecEnv([lambda: env]))
begin = 0
step_size = int(2e4)
# dirname = "models/"
# logdirname = "boardlog/"
# modeldir = "ppo2boxbestparam/"
# file_step = "2e4-"
# for f in listdir(dirname+modeldir):
# if f.startswith(file_step):
# end = f[len(file_step):]
# num = int(end[:len(end)-4])
# if num > begin:
# begin = num
# seed 420420420
# Instantiate the agent
# model = PPO2('MlpPolicy', env,verbose=1,max_grad_norm=1.42481794257356,cliprange=1.36870169927419, vf_coef=0.487354638658612, ent_coef=0.000130839434944482, gamma=0.993211512071304, lam=0.92669713813749, learning_rate=0.00150606967404027, n_steps=709, noptepochs=35,nminibatches=1)
# Train the agent
print("STARTING TO LEARN", begin)
model = PPO2.load("models/pretrain/50000_heutistic_pretrain_discrete.pkl",
env=env)
while True:
env.reset()
model.learn(total_timesteps=step_size, log_interval=100)
begin += 1
# Save the agent
# model.save(dirname+modeldir+file_step+str(begin))
print("saved learnstep", begin, "total iterations are now",
begin * step_size)