def _init():
env = environment(x, y, z, gamma, turnspc, policyname)
env.seed(seed + rank)
return env
env = environment(x, y, z, gamma, turnspc, policyname)
env.seed(seed + rank)
return env
set_global_seeds(seed)
return _init
if __name__ == '__main__':
num_cpu = ncpu # Number of processes to use
# Create the vectorized environment
env = SubprocVecEnv([make_env(x, y, z, i) for i in range(num_cpu)])
eval_env = evalenv(x, y, z, gamma, turnspc, policyname)
env1 = environment(x, y, z, gamma, turnspc, policyname)
# Stable Baselines provides you with make_vec_env() helper
# which does exactly the previous steps for you:
# env = make_vec_env(env_id, n_envs=num_cpu, seed=0)
#create callbacks to record data, initiate events during training.
callbacklist = CallbackList([
TimeLimit(episodetimesteps),
EvalCallback(eval_env,
log_path=evpath,
n_eval_episodes=100,
eval_freq=50000,
deterministic=False,
best_model_save_path=evpath),
EvalCallback(env1,
log_path=savepath,