def train():
if not os.path.isdir("log/"):
os.mkdir("log")
if ENV_COUNT == 1:
envs = create_env_headless()
env_id = str(time.time())[-6:]
envs = Monitor(envs,
"log/" + MODEL_NAME + "-" + env_id,
allow_early_resets=False)
vec_envs = DummyVecEnv([lambda: envs])
else:
vec_envs = []
def make_env():
env_id = str(time.time())[-6:]
env = create_env_headless()
return Monitor(env,
"log/" + MODEL_NAME + "-" + env_id,
allow_early_resets=False)
for _ in range(ENV_COUNT):
vec_envs.append(make_env)
vec_envs = SubprocVecEnv(vec_envs)
model = PPO2('CnnPolicy',
vec_envs,
verbose=1,
ent_coef=0.0001,
n_steps=256)
model.learn(total_timesteps=TIMESTEPS)
model.save(MODEL_NAME)
vec_envs.close()
print("Learning Done!")
def run_experiment(args):
randomization_settings = {
"engagement_distance": (100, 100),
"turnframes": (args.turnframes, args.turnframes)
}
if args.randomize_engagement:
randomization_settings["engagement_distance"] = (100, 200)
vecEnv = None
if args.num_envs == 1:
# Create dummyvecenv
env = gym.make(args.env)
env = Monitor(
TorilleWrapper(env, 100, args.experiment_name,
randomization_settings), args.experiment_name)
vecEnv = DummyVecEnv([
lambda: env
]) # The algorithms require a vectorized environment to run
else:
vecEnv = []
def make_env():
env = gym.make(args.env)
unique_id = str(time.time())[-6:]
experiment_env_name = args.experiment_name + ("_env%s" % unique_id)
return Monitor(
TorilleWrapper(env, 100, experiment_env_name,
randomization_settings), experiment_env_name)
for i in range(args.num_envs):
vecEnv.append(make_env)
vecEnv = SubprocVecEnv(vecEnv)
steps_per_env = args.steps_per_batch // args.num_envs
# Standard 2 x 64 network with sigmoid activations
policy_kwargs = dict(act_fun=tf.nn.sigmoid, net_arch=[64, 64])
model = None
if args.agent == "ppo":
model = PPO2(MlpPolicy,
vecEnv,
policy_kwargs=policy_kwargs,
ent_coef=args.ent_coef,
n_steps=steps_per_env,
verbose=1)
elif args.agent == "trpo":
model = TRPO(MlpPolicy,
vecEnv,
policy_kwargs=policy_kwargs,
entcoeff=args.ent_coef,
timesteps_per_batch=steps_per_env,
verbose=1)
model.learn(total_timesteps=args.timesteps)
