def run():
args = parser.parse_args()
args.task = 0
args.f_num = util.parse_to_num(args.f_num)
args.f_stride = util.parse_to_num(args.f_stride)
args.f_size = util.parse_to_num(args.f_size)
args.branch = util.parse_to_num(args.branch)
env = new_env(args)
args.meta_dim = 0 if not hasattr(env, 'meta') else len(env.meta())
device = '/gpu:0' if args.gpu > 0 else '/cpu:0'
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
config = tf.ConfigProto(device_filters=device,
gpu_options=gpu_options,
allow_soft_placement=True)
with tf.Session(config=config) as sess:
if args.alg == 'A3C':
model_type = 'policy'
elif args.alg == 'Q':
model_type = 'q'
elif args.alg == 'VPN':
model_type = 'vpn'
else:
raise ValueError('Invalid algorithm: ' + args.alg)
with tf.device(device):
with tf.variable_scope("local/learner"):
agent = eval("model." + args.model)(
env.observation_space.shape,
env.action_space.n,
type=model_type,
gamma=args.gamma,
dim=args.dim,
f_num=args.f_num,
f_stride=args.f_stride,
f_size=args.f_size,
f_pad=args.f_pad,
branch=args.branch,
meta_dim=args.meta_dim)
print("Num parameters: %d" % agent.num_param)
saver = tf.train.Saver()
saver.restore(sess, args.checkpoint)
np.random.seed(args.seed)
reward = evaluate(env, agent, args.n_play, eps=args.eps)
print("Reward: %.2f" % (reward))
def run():
args = parser.parse_args()
args.task = 0
args.f_num = util.parse_to_num(args.f_num)
args.f_stride = util.parse_to_num(args.f_stride)
args.f_size = util.parse_to_num(args.f_size)
args.branch = util.parse_to_num(args.branch)
env = new_env(args)
args.meta_dim = 0
device = '/gpu:0' if args.gpu > 0 else '/cpu:0'
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
config = tf.ConfigProto(device_filters=device,
gpu_options=gpu_options,
allow_soft_placement=True)
with tf.Session(config=config) as sess:
model_type = 'vpn'
with tf.device(device):
with tf.variable_scope("local/learner"):
agent = eval("model." + args.model)(
env.observation_space.shape,
env.action_space.n,
type=model_type,
gamma=args.gamma,
dim=args.dim,
f_num=args.f_num,
f_stride=args.f_stride,
f_size=args.f_size,
f_pad=args.f_pad,
branch=args.branch,
meta_dim=args.meta_dim)
print("Num parameters: %d" % agent.num_param)
saver = tf.train.Saver()
saver.restore(sess, args.checkpoint)
np.random.seed(args.seed)
evaluate(env, agent, args.n_play, eps=args.eps)
def run(envs=None):
stats = []
args = parser.parse_args()
args.task = 0
args.f_num = util.parse_to_num(args.f_num)
args.f_stride = util.parse_to_num(args.f_stride)
args.f_size = util.parse_to_num(args.f_size)
args.branch = util.parse_to_num(args.branch)
env = new_env(args)
if envs is None or not envs:
envs = [env]
args.meta_dim = 0 if env.meta() is None else len(env.meta())
device = '/gpu:0' if args.gpu > 0 else '/cpu:0'
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
branches = [
[4, 4, 4],
[4, 4, 4, 4],
[4, 4, 4, 4, 4],
[4, 1, 4, 1, 4],
[1],
[1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 4, 4, 1],
[4, 4, 4, 1, 1],
[4, 4, 4, 1, 1, 1],
[1, 4, 4, 4],
[1, 1, 1, 4, 4, 4],
[1, 1, 1, 4, 4, 4],
]
paths = [
f'/home/ikaynov/Repositories/value-prediction-network/Experiments/{x}/best'
for x in [
's10_t20_g8_444',
's10_t20_g8_4444',
's10_t20_g8_44444',
]
]
count = 0
count_max = len(paths) * len(branches)
for ck in paths:
for branch_type in branches:
print(f'Executing {count}/{count_max}.')
config = tf.ConfigProto(device_filters=device,
gpu_options=gpu_options,
allow_soft_placement=True)
tf.reset_default_graph()
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
if args.alg == 'A3C':
model_type = 'policy'
elif args.alg == 'Q':
model_type = 'q'
elif args.alg == 'VPN':
model_type = 'vpn'
else:
raise ValueError('Invalid algorithm: ' + args.alg)
with tf.device(device):
# np.random.seed(args.seed)
with tf.variable_scope("local/learner"):
agent = eval("model." + args.model)(
env.observation_space.shape,
env.action_space.n,
type=model_type,
gamma=args.gamma,
dim=args.dim,
f_num=args.f_num,
f_stride=args.f_stride,
f_size=args.f_size,
f_pad=args.f_pad,
branch=branch_type,
meta_dim=args.meta_dim)
agent.train_branch = str([
int(x)
for x in list(ck.split('/')[-2].split('_')[-1])
])
print("Num parameters: %d" % agent.num_param)
saver = tf.train.Saver()
saver.restore(sess, ck)
for i, env in enumerate(envs):
run_stats = evaluate(env,
agent,
args.n_play,
eps=args.eps)
stats += run_stats
count += 1
return stats