def train(params_dict: dict):
ncpu = multiprocessing.cpu_count()
# ncpu = 1
env_id = params_dict['env_params']['env']
total_timesteps = float(params_dict['training_params']['num_timesteps'])
learn = get_learn_function(params_dict['model_params']['alg'])
alg_kwargs = get_learn_function_defaults(
params_dict['model_params']['alg'], 'atari')
alg_kwargs['network'] = params_dict['model_params']['network']
alg_kwargs['lr'] = 0.0001
if 'frame_stack' in params_dict['env_params'] and params_dict[
'env_params']['frame_stack']:
wrapper_kwargs = {'frame_stack': True}
else:
wrapper_kwargs = {}
env = make_vec_env(env_id,
'atari',
ncpu,
seed=None,
wrapper_kwargs=wrapper_kwargs)
# env = VecFrameStack(env, 4)
model = learn(env=env,
seed=None,
total_timesteps=total_timesteps,
**alg_kwargs)
return model, env
def train(args, extra_args):
env_type, env_id = run.get_env_type(args.env)
if args.alg == 'gail':
env_type += '_gail'
args.alg = 'bgail'
elif args.alg not in ['bgail', 'gail']:
raise NotImplementedError
learn = run.get_learn_function(args.alg)
alg_kwargs = run.get_learn_function_defaults(args.alg, env_type)
alg_kwargs.update(extra_args)
env = build_env(args)
logger.configure(os.path.join("log", "GAIL", args.env, "subsample_{}".format(extra_args["data_subsample_freq"]),
"traj_{}".format(extra_args["num_expert_trajs"]), "batch_size_{}".format(extra_args["timesteps_per_batch"]),
"seed_{}".format(args.seed)))
print('Training {} on {}:{} with arguments \n{}'.format(args.alg, env_type, env_id, alg_kwargs))
model = learn(env=env,
seed=args.seed,
save_path=args.save_path,
load_path=args.load_path,
render=args.render,
**alg_kwargs)
def train(args, extra_args):
env_type, env_id = run.get_env_type(args.env)
if args.alg == 'gail':
env_type += '_gail'
args.alg = 'bgail'
elif args.alg not in ['bgail', 'gail']:
raise NotImplementedError
learn = run.get_learn_function(args.alg)
alg_kwargs = run.get_learn_function_defaults(args.alg, env_type)
alg_kwargs.update(extra_args)
env = build_env(args)
print('Training {} on {}:{} with arguments \n{}'.format(
args.alg, env_type, env_id, alg_kwargs))
model = learn(env=env,
seed=args.seed,
save_path=args.save_path,
load_path=args.load_path,
render=args.render,
**alg_kwargs)
'noptepochs': 10,
'save_interval': 20,
'log_interval': 1,
'save_path': save_path,
'model_load_path': model_load_path,
'seed': 0,
'reward_scale': 1,
'flatten_dict_observations': True,
'transfer_weights': False
}
args = SimpleNamespace(**args_dict)
# Prepare the environment and learning algorithm
env_type, env_id = get_env_type(args.env)
learn = get_learn_function(args.alg)
alg_kwargs = get_learn_function_defaults(args.alg, env_type)
env = build_env(args)
alg_kwargs['network'] = args.network
# The path we will store the results of this experiment
full_path = args.save_path + '/' + args.env + '-' + args.alg
# Make folders that we will store the checkpoints, models and epoch results
if not os.path.exists(full_path):
os.makedirs(full_path)
os.makedirs(full_path + '/checkpoints')
print("About to start learning model")
model = learn(env=env,
seed=args.seed,
