}
assert os.path.isfile(os.path.join(
PATHS['model'],
'best_model.zip')), "No model file found in %s" % PATHS['model']
assert os.path.isfile(
PATHS['scenerios_json_path']
), "No scenario file named %s" % PATHS['scenerios_json_path']
# initialize hyperparams
params = load_hyperparameters_json(agent_hyperparams, PATHS)
print("START RUNNING AGENT: %s" % params['agent_name'])
print_hyperparameters(params)
# initialize task manager
task_manager = get_predefined_task(mode='ScenerioTask', PATHS=PATHS)
# initialize gym env
env = DummyVecEnv([
lambda: FlatlandEnv(task_manager,
PATHS.get('robot_setting'),
PATHS.get('robot_as'),
params['reward_fnc'],
params['discrete_action_space'],
goal_radius=0.50,
max_steps_per_episode=350)
])
if params['normalize']:
env = VecNormalize(env,
training=False,
norm_obs=True,
norm_reward=False,
# initialize hyperparameters (save to/ load from json)
hyperparams_obj = agent_hyperparams(AGENT_NAME, robot, gamma, n_steps,
ent_coef, learning_rate, vf_coef,
max_grad_norm, gae_lambda, batch_size,
n_epochs, clip_range, reward_fnc,
discrete_action_space, normalize,
task_mode, start_stage)
params = initialize_hyperparameters(agent_name=AGENT_NAME,
PATHS=PATHS,
hyperparams_obj=hyperparams_obj,
load_target=args.load)
# instantiate gym environment
n_envs = 1
task_manager = get_predefined_task(params['task_mode'],
params['curr_stage'], PATHS)
env = DummyVecEnv([
lambda: FlatlandEnv(task_manager,
PATHS.get('robot_setting'),
PATHS.get('robot_as'),
params['reward_fnc'],
params['discrete_action_space'],
goal_radius=1.00,
max_steps_per_episode=200)
] * n_envs)
if params['normalize']:
env = VecNormalize(env,
training=True,
norm_obs=True,
norm_reward=False,
clip_reward=15)
# generate agent name and model specific paths
AGENT_NAME = get_agent_name(args)
print("________ STARTING TRAINING WITH: %s ________\n" % AGENT_NAME)
PATHS = get_paths(AGENT_NAME, args)
if args.n is None:
n_timesteps = 6000
else:
n_timesteps = args.n
# instantiate gym environment
n_envs = 1
task = get_predefined_task("random")
env = DummyVecEnv([lambda: FlatlandEnv(task, PATHS.get('robot_setting'), PATHS.get('robot_as'), discrete_action_space)] * n_envs)
# instantiate eval environment
eval_env = Monitor(FlatlandEnv(task, PATHS.get('robot_setting'), PATHS.get('robot_as'), discrete_action_space), PATHS.get('eval'))
eval_env = EvalCallback(eval_env, n_eval_episodes=10, eval_freq=250, log_path=PATHS.get('eval'), best_model_save_path=PATHS.get('model'), deterministic=True)
# determine mode
if args.custom_mlp:
# custom mlp flag
model = PPO("MlpPolicy", env, policy_kwargs = dict(net_arch = args.net_arch, activation_fn = get_act_fn(args.act_fn)),
gamma = gamma, n_steps = n_steps, ent_coef = ent_coef, learning_rate = learning_rate, vf_coef = vf_coef,
max_grad_norm = max_grad_norm, gae_lambda = gae_lambda, batch_size = batch_size, n_epochs = n_epochs, clip_range = clip_range,
tensorboard_log = PATHS.get('tb'), verbose = 1)