def test_merge_config():
conf = get_config()
conf = merge_config(conf, {"conf1": 2, "conf3": 3})
assert conf.conf1 == 2
assert conf.conf2 == 2
assert conf.conf3 == 3
conf = get_config()
conf2 = Config(conf1=2, conf3=3)
conf = merge_config(conf, conf2)
assert conf.conf1 == 2
assert conf.conf2 == 2
assert conf.conf3 == 3
def train_config(self):
disable_view_window()
c = Config()
c.env_name = "Pendulum-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 3
c.action_dim = 1
c.action_range = 2
c.max_episodes = 1000
c.max_steps = 200
c.noise_param = (0, 0.2)
c.noise_mode = "normal"
c.noise_interval = 2
c.replay_size = 100000
c.solved_reward = -400
c.solved_repeat = 5
return c
class TestIMPALA(object):
# configs and definitions
disable_view_window()
c = Config()
# Note: online policy algorithms such as PPO and A3C does not
# work well in Pendulum (reason unknown)
# and MountainCarContinuous (sparse returns)
c.env_name = "CartPole-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 4
c.action_num = 2
c.max_episodes = 2000
c.max_steps = 200
c.replay_size = 10000
c.solved_reward = 150
c.solved_repeat = 5
@staticmethod
def impala(device, dtype, use_lr_sch=False):
c = TestIMPALA.c
actor = smw(
Actor(c.observe_dim, c.action_num).type(dtype).to(device), device,
device)
critic = smw(
Critic(c.observe_dim).type(dtype).to(device), device, device)
servers = model_server_helper(model_num=1)
world = get_world()
# process 0 and 1 will be workers, and 2 will be trainer
impala_group = world.create_rpc_group("impala", ["0", "1", "2"])
if use_lr_sch:
lr_func = gen_learning_rate_func([(0, 1e-3), (200000, 3e-4)],
logger=default_logger)
impala = IMPALA(actor,
critic,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
impala_group,
servers,
lr_scheduler=LambdaLR,
lr_scheduler_args=((lr_func, ), (lr_func, )))
else:
impala = IMPALA(actor, critic, t.optim.Adam,
nn.MSELoss(reduction='sum'), impala_group, servers)
return impala
########################################################################
# Test for IMPALA acting
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180)
@WorldTestBase.setup_world
def test_act(_, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
impala.act({"state": state})
return True
########################################################################
# Test for IMPALA action evaluation
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180)
@WorldTestBase.setup_world
def test_eval_action(_, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
impala._eval_act({"state": state}, {"action": action})
return True
########################################################################
# Test for IMPALA criticizing
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180)
@WorldTestBase.setup_world
def test__criticize(_, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
impala._criticize({"state": state})
return True
########################################################################
# Test for IMPALA storage
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180)
@WorldTestBase.setup_world
def test_store_step(_, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
old_state = state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
with pytest.raises(NotImplementedError):
impala.store_transition({
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"action_log_prob": 0.1,
"terminal": False
})
return True
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180)
@WorldTestBase.setup_world
def test_store_episode(_, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
old_state = state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
episode = [{
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"action_log_prob": 0.1,
"terminal": False
} for _ in range(3)]
impala.store_episode(episode)
return True
########################################################################
# Test for IMPALA update
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180)
@WorldTestBase.setup_world
def test_update(rank, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
old_state = state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
if rank == 0:
# episode length = 3
impala.store_episode([{
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"action_log_prob": 0.1,
"terminal": False
} for _ in range(3)])
elif rank == 1:
# episode length = 2
impala.store_episode([{
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"action_log_prob": 0.1,
"terminal": False
} for _ in range(2)])
if rank == 2:
sleep(2)
impala.update(update_value=True,
update_target=True,
concatenate_samples=True)
return True
########################################################################
# Test for IMPALA save & load
########################################################################
# Skipped, it is the same as base framework
########################################################################
# Test for IMPALA lr_scheduler
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180)
@WorldTestBase.setup_world
def test_lr_scheduler(_, device, dtype):
impala = TestIMPALA.impala(device, dtype)
impala.update_lr_scheduler()
return True
########################################################################
# Test for IMPALA full training.
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=1800)
@WorldTestBase.setup_world
def test_full_train(rank):
c = TestIMPALA.c
impala = TestIMPALA.impala("cpu", t.float32)
# perform manual syncing to decrease the number of rpc calls
impala.set_sync(False)
# begin training
episode, step = Counter(), Counter()
reward_fulfilled = Counter()
smoother = Smooth()
terminal = False
env = c.env
world = get_world()
all_group = world.create_rpc_group("all", ["0", "1", "2"])
all_group.pair("{}_running".format(rank), True)
default_logger.info("{}, pid {}".format(rank, os.getpid()))
if rank == 0:
all_group.pair("episode", episode)
if rank in (0, 1):
while episode < c.max_episodes:
# wait for trainer to keep up
sleep(0.2)
episode.count()
# batch size = 1
total_reward = 0
state = t.tensor(env.reset(), dtype=t.float32)
impala.manual_sync()
tmp_observations = []
while not terminal and step <= c.max_steps:
step.count()
with t.no_grad():
old_state = state
action, action_log_prob, *_ = impala.act(
{"state": old_state.unsqueeze(0)})
state, reward, terminal, _ = env.step(action.item())
state = t.tensor(state, dtype=t.float32).flatten()
total_reward += float(reward)
tmp_observations.append({
"state": {
"state": old_state.unsqueeze(0)
},
"action": {
"action": action
},
"next_state": {
"state": state.unsqueeze(0)
},
"reward":
float(reward),
"action_log_prob":
action_log_prob.item(),
"terminal":
terminal or step == c.max_steps
})
impala.store_episode(tmp_observations)
smoother.update(total_reward)
step.reset()
terminal = False
default_logger.info("Process {} Episode {} "
"total reward={:.2f}".format(
rank, episode, smoother.value))
if smoother.value > c.solved_reward:
reward_fulfilled.count()
if reward_fulfilled >= c.solved_repeat:
default_logger.info("Environment solved!")
all_group.unpair("{}_running".format(rank))
while (all_group.is_paired("0_running")
or all_group.is_paired("1_running")):
# wait for all workers to join
sleep(1)
# wait for trainer
sleep(5)
return True
else:
reward_fulfilled.reset()
else:
# wait for some samples
# Note: the number of entries in buffer means "episodes"
# rather than steps here!
while impala.replay_buffer.all_size() < 5:
sleep(0.1)
while (all_group.is_paired("0_running")
or all_group.is_paired("1_running")):
impala.update()
return True
raise RuntimeError("IMPALA Training failed.")
def train_config(self, gpu):
disable_view_window()
c = Config()
# Note: online policy algorithms such as PPO and A2C does not
# work well in Pendulum (reason unknown)
# and MountainCarContinuous (sparse returns)
c.env_name = "CartPole-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 4
c.action_num = 2
# maximum and minimum of reward value
# since reward is 1 for every step, maximum q value should be
# below 20(reward_future_steps) * (1 + discount ** n_steps) < 40
c.value_max = 40
c.value_min = 0
c.reward_future_steps = 20
c.max_episodes = 1000
c.max_steps = 200
c.replay_size = 100000
# RAINBOW is not very stable (without dueling and noisy linear)
# compared to other DQNs
c.solved_reward = 180
c.solved_repeat = 5
c.device = gpu
return c
def train_config(self, pytestconfig):
disable_view_window()
c = Config()
# Note: online policy algorithms such as PPO and A2C does not
# work well in Pendulum (reason unknown)
# and MountainCarContinuous (sparse returns)
c.env_name = "CartPole-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 4
c.action_num = 2
c.max_episodes = 1000
c.max_steps = 200
c.replay_size = 100000
c.solved_reward = 190
c.solved_repeat = 5
c.device = "cpu"
return c
class TestDQNApex(object):
# configs and definitions
disable_view_window()
c = Config()
# Note: online policy algorithms such as PPO and A2C does not
# work well in Pendulum (reason unknown)
# and MountainCarContinuous (sparse returns)
c.env_name = "CartPole-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 4
c.action_num = 2
c.max_episodes = 2000
c.max_steps = 200
c.replay_size = 100000
c.solved_reward = 190
c.solved_repeat = 5
@staticmethod
def dqn_apex():
c = TestDQNApex.c
q_net = smw(
QNet(c.observe_dim, c.action_num).to(c.device), c.device, c.device)
q_net_t = smw(
QNet(c.observe_dim, c.action_num).to(c.device), c.device, c.device)
servers = model_server_helper(model_num=1)
world = get_world()
# process 0 and 1 will be workers, and 2 will be trainer
apex_group = world.create_rpc_group("apex", ["0", "1", "2"])
dqn_apex = DQNApex(q_net,
q_net_t,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
apex_group,
servers,
replay_device="cpu",
replay_size=c.replay_size)
return dqn_apex
########################################################################
# Test for DQNApex acting
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["gpu"],
timeout=180)
@WorldTestBase.setup_world
def test_act(_, gpu):
c = TestDQNApex.c
c.device = gpu
dqn_apex = TestDQNApex.dqn_apex()
state = t.zeros([1, c.observe_dim])
dqn_apex.act_discrete({"state": state})
dqn_apex.act_discrete({"state": state}, True)
dqn_apex.act_discrete_with_noise({"state": state})
dqn_apex.act_discrete_with_noise({"state": state}, True)
return True
########################################################################
# Test for DQNApex criticizing
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["gpu"],
timeout=180)
@WorldTestBase.setup_world
def test_criticize(_, gpu):
c = TestDQNApex.c
c.device = gpu
dqn_apex = TestDQNApex.dqn_apex()
state = t.zeros([1, c.observe_dim])
dqn_apex._criticize({"state": state})
dqn_apex._criticize({"state": state}, True)
return True
########################################################################
# Test for DQNApex storage
########################################################################
# Skipped, it is the same as DQN
########################################################################
# Test for DQNApex update
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["gpu"],
timeout=180)
@WorldTestBase.setup_world
def test_update(rank, gpu):
c = TestDQNApex.c
c.device = gpu
dqn_apex = TestDQNApex.dqn_apex()
old_state = state = t.zeros([1, c.observe_dim])
action = t.zeros([1, 1], dtype=t.int)
if rank in (0, 1):
dqn_apex.store_episode([{
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"terminal": False
} for _ in range(3)])
dqn_apex.manual_sync()
if rank == 2:
sleep(2)
dqn_apex.update(update_value=True,
update_target=True,
concatenate_samples=True)
return True
########################################################################
# Test for DQNApex save & load
########################################################################
# Skipped, it is the same as DQN
########################################################################
# Test for DQNApex lr_scheduler
########################################################################
# Skipped, it is the same as DQN
########################################################################
# Test for DQNApex full training.
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["gpu"],
timeout=1800)
@WorldTestBase.setup_world
def test_full_train(rank, gpu):
c = TestDQNApex.c
c.device = gpu
dqn_apex = TestDQNApex.dqn_apex()
# perform manual syncing to decrease the number of rpc calls
dqn_apex.set_sync(False)
# begin training
episode, step = Counter(), Counter()
reward_fulfilled = Counter()
smoother = Smooth()
terminal = False
env = c.env
world = get_world()
all_group = world.create_rpc_group("all", ["0", "1", "2"])
all_group.pair("{}_running".format(rank), True)
if rank in (0, 1):
while episode < c.max_episodes:
# wait for trainer to keep up
sleep(0.2)
episode.count()
# batch size = 1
total_reward = 0
state = t.tensor(env.reset(), dtype=t.float32, device=c.device)
dqn_apex.manual_sync()
while not terminal and step <= c.max_steps:
step.count()
with t.no_grad():
old_state = state
# agent model inference
action = dqn_apex.act_discrete_with_noise(
{"state": old_state.unsqueeze(0)})
state, reward, terminal, _ = env.step(action.item())
state = t.tensor(state,
dtype=t.float32,
device=c.device).flatten()
total_reward += float(reward)
dqn_apex.store_transition({
"state": {
"state": old_state.unsqueeze(0)
},
"action": {
"action": action
},
"next_state": {
"state": state.unsqueeze(0)
},
"reward":
float(reward),
"terminal":
terminal or step == c.max_steps
})
smoother.update(total_reward)
step.reset()
terminal = False
default_logger.info(
"Process {} Episode {} total reward={:.2f}".format(
rank, episode, smoother.value))
if smoother.value > c.solved_reward:
reward_fulfilled.count()
if reward_fulfilled >= c.solved_repeat:
default_logger.info("Environment solved!")
all_group.unpair("{}_running".format(rank))
while (all_group.is_paired("0_running")
or all_group.is_paired("1_running")):
# wait for all workers to join
sleep(1)
# wait for trainer
sleep(5)
return True
else:
reward_fulfilled.reset()
else:
# wait for some samples
while dqn_apex.replay_buffer.all_size() < 500:
sleep(0.1)
while (all_group.is_paired("0_running")
or all_group.is_paired("1_running")):
dqn_apex.update()
return True
raise RuntimeError("DQN-Apex Training failed.")
class TestDDPGApex(object):
# configs and definitions
disable_view_window()
c = Config()
c.env_name = "Pendulum-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 3
c.action_dim = 1
c.action_range = 2
c.max_episodes = 2000
c.max_steps = 200
c.noise_param = (0, 0.2)
c.noise_mode = "normal"
c.replay_size = 100000
# takes too much computing resource
# decrease standard for faster validation
c.solved_reward = -300
c.solved_repeat = 5
@staticmethod
def ddpg_apex(discrete=False):
c = TestDDPGApex.c
if not discrete:
actor = smw(
Actor(c.observe_dim, c.action_dim,
c.action_range).to(c.device), c.device, c.device)
actor_t = smw(
Actor(c.observe_dim, c.action_dim,
c.action_range).to(c.device), c.device, c.device)
else:
actor = smw(
ActorDiscrete(c.observe_dim, c.action_dim).to(c.device),
c.device, c.device)
actor_t = smw(
ActorDiscrete(c.observe_dim, c.action_dim).to(c.device),
c.device, c.device)
critic = smw(
Critic(c.observe_dim, c.action_dim).to(c.device), c.device,
c.device)
critic_t = smw(
Critic(c.observe_dim, c.action_dim).to(c.device), c.device,
c.device)
servers = model_server_helper(model_num=2)
world = get_world()
# process 0 and 1 will be workers, and 2 will be trainer
apex_group = world.create_rpc_group("worker", ["0", "1", "2"])
ddpg_apex = DDPGApex(actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
apex_group,
servers,
replay_device="cpu",
replay_size=c.replay_size)
return ddpg_apex
########################################################################
# Test for DDPGApex contiguous domain acting
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["gpu"],
timeout=180)
@WorldTestBase.setup_world
def test_contiguous_act(_, gpu):
c = TestDDPGApex.c
c.device = gpu
ddpg_apex = TestDDPGApex.ddpg_apex()
state = t.zeros([1, c.observe_dim])
ddpg_apex.act({"state": state})
ddpg_apex.act({"state": state}, use_target=True)
ddpg_apex.act_with_noise({"state": state},
noise_param=(0, 1.0),
mode="uniform")
ddpg_apex.act_with_noise({"state": state},
noise_param=(0, 1.0),
mode="uniform",
use_target=True)
return True
########################################################################
# Test for DDPGApex discrete domain acting
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["gpu"],
timeout=180)
@WorldTestBase.setup_world
def test_discrete_act(_, gpu):
c = TestDDPGApex.c
c.device = gpu
ddpg_apex = TestDDPGApex.ddpg_apex(discrete=True)
state = t.zeros([1, c.observe_dim])
ddpg_apex.act_discrete({"state": state})
ddpg_apex.act_discrete({"state": state}, use_target=True)
ddpg_apex.act_discrete_with_noise({"state": state})
ddpg_apex.act_discrete_with_noise({"state": state}, use_target=True)
return True
########################################################################
# Test for DDPGApex criticizing
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["gpu"],
timeout=180)
@WorldTestBase.setup_world
def test__criticize(_, gpu):
c = TestDDPGApex.c
c.device = gpu
ddpg_apex = TestDDPGApex.ddpg_apex()
state = t.zeros([1, c.observe_dim])
action = t.zeros([1, c.action_dim])
ddpg_apex._criticize({"state": state}, {"action": action})
ddpg_apex._criticize({"state": state}, {"action": action},
use_target=True)
return True
########################################################################
# Test for DDPGApex storage
########################################################################
# Skipped, it is the same as DDPG
########################################################################
# Test for DDPGApex update
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["gpu"],
timeout=180)
@WorldTestBase.setup_world
def test_update(rank, gpu):
c = TestDDPGApex.c
c.device = gpu
ddpg_apex = TestDDPGApex.ddpg_apex()
old_state = state = t.zeros([1, c.observe_dim])
action = t.zeros([1, c.action_dim])
if rank in (0, 1):
ddpg_apex.store_transition({
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"terminal": False
})
sleep(5)
ddpg_apex.manual_sync()
if rank == 2:
sleep(2)
ddpg_apex.update(update_value=True,
update_policy=True,
update_target=True,
concatenate_samples=True)
return True
########################################################################
# Test for DDPGApex save & load
########################################################################
# Skipped, it is the same as DDPG
########################################################################
# Test for DDPGApex lr_scheduler
########################################################################
# Skipped, it is the same as DDPG
########################################################################
# Test for DDPGApex full training.
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True],
pass_through=["gpu"],
timeout=1800)
@WorldTestBase.setup_world
def test_full_train(rank, gpu):
c = TestDDPGApex.c
c.device = gpu
ddpg_apex = TestDDPGApex.ddpg_apex()
# perform manual syncing to decrease the number of rpc calls
ddpg_apex.set_sync(False)
# begin training
episode, step = Counter(), Counter()
reward_fulfilled = Counter()
smoother = Smooth()
terminal = False
env = c.env
world = get_world()
all_group = world.create_rpc_group("all", ["0", "1", "2"])
all_group.pair("{}_running".format(rank), True)
default_logger.info("{}, pid {}".format(rank, os.getpid()))
if rank == 0:
all_group.pair("episode", episode)
if rank in (0, 1):
while episode < c.max_episodes:
# wait for trainer to keep up
sleep(0.2)
episode.count()
# batch size = 1
total_reward = 0
state = t.tensor(env.reset(), dtype=t.float32, device=c.device)
ddpg_apex.manual_sync()
while not terminal and step <= c.max_steps:
step.count()
with t.no_grad():
old_state = state
action = ddpg_apex.act_with_noise(
{"state": old_state.unsqueeze(0)},
noise_param=c.noise_param,
mode=c.noise_mode)
state, reward, terminal, _ = env.step(
action.cpu().numpy())
state = t.tensor(state,
dtype=t.float32,
device=c.device).flatten()
total_reward += float(reward)
ddpg_apex.store_transition({
"state": {
"state": old_state.unsqueeze(0)
},
"action": {
"action": action
},
"next_state": {
"state": state.unsqueeze(0)
},
"reward":
float(reward),
"terminal":
terminal or step == c.max_steps
})
smoother.update(total_reward)
step.reset()
terminal = False
default_logger.info("Process {} Episode {} "
"total reward={:.2f}".format(
rank, episode, smoother.value))
if smoother.value > c.solved_reward:
reward_fulfilled.count()
if reward_fulfilled >= c.solved_repeat:
default_logger.info("Environment solved!")
all_group.unpair("{}_running".format(rank))
while (all_group.is_paired("0_running")
or all_group.is_paired("1_running")):
# wait for all workers to join
sleep(1)
# wait for trainer
sleep(5)
return True
else:
reward_fulfilled.reset()
else:
# wait for some samples
while ddpg_apex.replay_buffer.all_size() < 500:
sleep(0.1)
while (all_group.is_paired("0_running")
or all_group.is_paired("1_running")):
ddpg_apex.update()
return True
raise RuntimeError("DDPG-Apex Training failed.")
def get_config():
c = Config()
c.conf1 = 1
c.conf2 = 2
return c
class TestA3C:
# configs and definitions
disable_view_window()
c = Config()
# Note: online policy algorithms such as PPO and A3C does not
# work well in Pendulum (reason unknown)
# and MountainCarContinuous (sparse returns)
c.env_name = "CartPole-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 4
c.action_num = 2
c.max_episodes = 3000
c.max_steps = 200
c.replay_size = 10000
c.solved_reward = 150
c.solved_repeat = 5
@staticmethod
def a3c(device, dtype):
c = TestA3C.c
actor = smw(
Actor(c.observe_dim, c.action_num).type(dtype).to(device), device,
device)
critic = smw(
Critic(c.observe_dim).type(dtype).to(device), device, device)
# in all test scenarios, all processes will be used as reducers
servers = grad_server_helper(
[
lambda: Actor(c.observe_dim, c.action_num),
lambda: Critic(c.observe_dim)
],
learning_rate=5e-3,
)
a3c = A3C(
actor,
critic,
nn.MSELoss(reduction="sum"),
servers,
replay_device="cpu",
replay_size=c.replay_size,
)
return a3c
########################################################################
# Test for A3C acting
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_act(_, device, dtype):
c = TestA3C.c
a3c = TestA3C.a3c(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
a3c.act({"state": state})
return True
########################################################################
# Test for A3C action evaluation
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_eval_action(_, device, dtype):
c = TestA3C.c
a3c = TestA3C.a3c(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
a3c._eval_act({"state": state}, {"action": action})
return True
########################################################################
# Test for A3C criticizing
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test__criticize(_, device, dtype):
c = TestA3C.c
a3c = TestA3C.a3c(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
a3c._criticize({"state": state})
return True
########################################################################
# Test for A3C storage
########################################################################
# Skipped, it is the same as A2C
########################################################################
# Test for A3C update
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_update(rank, device, dtype):
c = TestA3C.c
c.device = gpu
a3c = TestA3C.a3c(device, dtype)
old_state = state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
begin = time()
while time() - begin < 5:
a3c.store_episode([{
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"terminal": False,
} for _ in range(3)])
a3c.update(
update_value=True,
update_policy=True,
update_target=True,
concatenate_samples=True,
)
sleep(0.01)
if rank == 1:
# pull the newest model
a3c.manual_sync()
return True
########################################################################
# Test for A3C save & load
########################################################################
# Skipped, it is the same as A2C
########################################################################
# Test for A3C lr_scheduler
########################################################################
# Skipped, it is the same as A2C
########################################################################
# Test for A3C config & init
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=180)
@WorldTestBase.setup_world
def test_config_init(rank):
c = TestA3C.c
config = A3C.generate_config({})
config["frame_config"]["models"] = ["Actor", "Critic"]
config["frame_config"]["model_kwargs"] = [
{
"state_dim": c.observe_dim,
"action_num": c.action_num
},
{
"state_dim": c.observe_dim
},
]
a3c = A3C.init_from_config(config)
old_state = state = t.zeros([1, c.observe_dim], dtype=t.float32)
action = t.zeros([1, 1], dtype=t.int)
begin = time()
while time() - begin < 5:
a3c.store_episode([{
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"terminal": False,
} for _ in range(3)])
a3c.update()
sleep(0.01)
if rank == 1:
# pull the newest model
a3c.manual_sync()
return True
########################################################################
# Test for A3C full training.
########################################################################
@staticmethod
@pytest.mark.parametrize("gae_lambda", [0.0, 0.5, 1.0])
@run_multi(expected_results=[True, True, True],
pass_through=["gae_lambda"],
timeout=1800)
@WorldTestBase.setup_world
def test_full_train(rank, gae_lambda):
c = TestA3C.c
a3c = TestA3C.a3c("cpu", t.float32)
a3c.set_sync(False)
# begin training
episode, step = Counter(), Counter()
reward_fulfilled = Counter()
smoother = Smooth()
terminal = False
env = c.env
# for cpu usage viewing
default_logger.info(f"{rank}, pid {os.getpid()}")
while episode < c.max_episodes:
episode.count()
# batch size = 1
total_reward = 0
state = t.tensor(env.reset(), dtype=t.float32)
a3c.manual_sync()
tmp_observations = []
while not terminal and step <= c.max_steps:
step.count()
with t.no_grad():
old_state = state
# agent model inference
action = a3c.act({"state": old_state.unsqueeze(0)})[0]
state, reward, terminal, _ = env.step(action.item())
state = t.tensor(state, dtype=t.float32).flatten()
total_reward += float(reward)
tmp_observations.append({
"state": {
"state": old_state.unsqueeze(0)
},
"action": {
"action": action
},
"next_state": {
"state": state.unsqueeze(0)
},
"reward":
float(reward),
"terminal":
terminal or step == c.max_steps,
})
# update
a3c.store_episode(tmp_observations)
a3c.update()
smoother.update(total_reward)
step.reset()
terminal = False
default_logger.info(
f"Process {rank} Episode {episode} total reward={smoother.value:.2f}"
)
if smoother.value > c.solved_reward:
reward_fulfilled.count()
if reward_fulfilled >= c.solved_repeat:
default_logger.info("Environment solved!")
return True
else:
reward_fulfilled.reset()
raise RuntimeError("A3C Training failed.")
class TestIMPALA:
# configs and definitions
disable_view_window()
c = Config()
# Note: online policy algorithms such as PPO and A3C does not
# work well in Pendulum (reason unknown)
# and MountainCarContinuous (sparse returns)
c.env_name = "CartPole-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 4
c.action_num = 2
c.max_episodes = 20000
c.max_steps = 200
c.replay_size = 10000
c.solved_reward = 150
c.solved_repeat = 5
@staticmethod
def impala(device, dtype, use_lr_sch=False):
c = TestIMPALA.c
actor = smw(
Actor(c.observe_dim, c.action_num).type(dtype).to(device), device, device
)
critic = smw(Critic(c.observe_dim).type(dtype).to(device), device, device)
servers = model_server_helper(model_num=1)
world = get_world()
# process 0 and 1 will be workers, and 2 will be trainer
impala_group = world.create_rpc_group("impala", ["0", "1", "2"])
if use_lr_sch:
lr_func = gen_learning_rate_func(
[(0, 1e-3), (200000, 3e-4)], logger=default_logger
)
impala = IMPALA(
actor,
critic,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
impala_group,
servers,
lr_scheduler=LambdaLR,
lr_scheduler_args=((lr_func,), (lr_func,)),
)
else:
impala = IMPALA(
actor,
critic,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
impala_group,
servers,
)
return impala
########################################################################
# Test for IMPALA acting
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@setup_world
def test_act(_, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
impala.act({"state": state})
return True
########################################################################
# Test for IMPALA action evaluation
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@setup_world
def test_eval_action(_, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
impala._eval_act({"state": state}, {"action": action})
return True
########################################################################
# Test for IMPALA criticizing
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@setup_world
def test__criticize(_, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
impala._criticize({"state": state})
return True
########################################################################
# Test for IMPALA storage
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@setup_world
def test_store_episode(_, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
old_state = state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
episode = [
{
"state": {"state": old_state},
"action": {"action": action},
"next_state": {"state": state},
"reward": 0,
"action_log_prob": 0.1,
"terminal": False,
}
for _ in range(3)
]
impala.store_episode(episode)
return True
########################################################################
# Test for IMPALA update
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@setup_world
def test_update(rank, device, dtype):
c = TestIMPALA.c
impala = TestIMPALA.impala(device, dtype)
old_state = state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
if rank == 0:
# episode length = 3
impala.store_episode(
[
{
"state": {"state": old_state},
"action": {"action": action},
"next_state": {"state": state},
"reward": 0,
"action_log_prob": 0.1,
"terminal": False,
}
for _ in range(3)
]
)
elif rank == 1:
# episode length = 2
impala.store_episode(
[
{
"state": {"state": old_state},
"action": {"action": action},
"next_state": {"state": state},
"reward": 0,
"action_log_prob": 0.1,
"terminal": False,
}
for _ in range(2)
]
)
if rank == 2:
sleep(2)
impala.update(
update_value=True, update_target=True, concatenate_samples=True
)
return True
########################################################################
# Test for IMPALA save & load
########################################################################
# Skipped, it is the same as base framework
########################################################################
# Test for IMPALA lr_scheduler
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@setup_world
def test_lr_scheduler(_, device, dtype):
impala = TestIMPALA.impala(device, dtype)
impala.update_lr_scheduler()
return True
########################################################################
# Test for IMPALA config & init
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=180)
@setup_world
def test_config_init(rank):
c = TestIMPALA.c
config = IMPALA.generate_config({})
config["frame_config"]["models"] = ["Actor", "Critic"]
config["frame_config"]["model_kwargs"] = [
{"state_dim": c.observe_dim, "action_num": c.action_num},
{"state_dim": c.observe_dim},
]
impala = IMPALA.init_from_config(config)
old_state = state = t.zeros([1, c.observe_dim], dtype=t.float32)
action = t.zeros([1, 1], dtype=t.int)
if rank == 0:
# episode length = 3
impala.store_episode(
[
{
"state": {"state": old_state},
"action": {"action": action},
"next_state": {"state": state},
"reward": 0,
"action_log_prob": 0.1,
"terminal": False,
}
for _ in range(3)
]
)
elif rank == 1:
# episode length = 2
impala.store_episode(
[
{
"state": {"state": old_state},
"action": {"action": action},
"next_state": {"state": state},
"reward": 0,
"action_log_prob": 0.1,
"terminal": False,
}
for _ in range(2)
]
)
if rank == 2:
sleep(2)
impala.update(
update_value=True, update_target=True, concatenate_samples=True
)
return True
########################################################################
# Test for IMPALA full training.
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=1800)
@setup_world
def test_full_train(rank):
training_group = get_world().create_rpc_group("training", ["0", "1", "2"])
c = TestIMPALA.c
impala = TestIMPALA.impala("cpu", t.float32)
# perform manual syncing to decrease the number of rpc calls
impala.set_sync(False)
# begin training
episode, step = Counter(), Counter()
reward_fulfilled = Counter()
smoother = Smooth()
terminal = False
env = c.env
env.seed(rank)
# make sure all things are initialized.
training_group.barrier()
# for cpu usage viewing
default_logger.info(f"{rank}, pid {os.getpid()}")
while episode < c.max_episodes:
episode.count()
if rank in (0, 1):
# batch size = 1
total_reward = 0
state = t.tensor(env.reset(), dtype=t.float32)
impala.manual_sync()
tmp_observations = []
while not terminal and step <= c.max_steps:
step.count()
with t.no_grad():
old_state = state
action, action_log_prob, *_ = impala.act(
{"state": old_state.unsqueeze(0)}
)
state, reward, terminal, _ = env.step(action.item())
state = t.tensor(state, dtype=t.float32).flatten()
total_reward += float(reward)
tmp_observations.append(
{
"state": {"state": old_state.unsqueeze(0)},
"action": {"action": action},
"next_state": {"state": state.unsqueeze(0)},
"reward": float(reward),
"action_log_prob": action_log_prob.item(),
"terminal": terminal or step == c.max_steps,
}
)
impala.store_episode(tmp_observations)
smoother.update(total_reward)
step.reset()
terminal = False
default_logger.info(
"Process {} Episode {} "
"total reward={:.2f}".format(rank, episode, smoother.value)
)
if smoother.value > c.solved_reward:
reward_fulfilled.count()
if reward_fulfilled >= c.solved_repeat:
default_logger.info("Environment solved!")
try:
training_group.pair(f"solved", True)
except KeyError:
# already solved in another process
pass
else:
reward_fulfilled.reset()
else:
# wait for some samples
if episode.get() > 200:
for _ in range(100):
impala.update()
default_logger.info("Updated 100 times.")
training_group.barrier()
if training_group.is_paired("solved"):
return True
raise RuntimeError("IMPALA Training failed.")
def train_config(self):
disable_view_window()
c = Config()
# the cooperative environment environment provided in
# https://github.com/openai/multiagent-particle-envs
c.env_name = "simple_spread"
c.env = create_env(c.env_name)
c.env.discrete_action_input = True
c.agent_num = 3
c.action_num = c.env.action_space[0].n
c.observe_dim = c.env.observation_space[0].shape[0]
# for contiguous tests
c.test_action_dim = 5
c.test_action_range = 1
c.test_observe_dim = 5
c.test_agent_num = 3
c.max_episodes = 1000
c.max_steps = 200
c.replay_size = 100000
# from https://github.com/wsjeon/maddpg-rllib/tree/master/plots
# PROBLEM: I have no idea how they calculate the rewards
# I cannot replicate their reward curve
c.solved_reward = -15
c.solved_repeat = 5
return c
class TestDDPGApex:
# configs and definitions
disable_view_window()
c = Config()
# Use cartpole-v0 instead since pendulum training is too slow on test machine.
c.env_name = "CartPole-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 4
# use dim instead of num here
c.action_dim = 2
c.action_range = 1
c.max_episodes = 20000
c.max_steps = 200
c.replay_size = 100000
c.solved_reward = 150
c.solved_repeat = 5
# only for continuous mode testings
c.action_range = 1
@staticmethod
def ddpg_apex(device, dtype, discrete=False):
c = TestDDPGApex.c
if not discrete:
actor = smw(
Actor(c.observe_dim, c.action_dim,
c.action_range).type(dtype).to(device),
device,
device,
)
actor_t = smw(
Actor(c.observe_dim, c.action_dim,
c.action_range).type(dtype).to(device),
device,
device,
)
else:
actor = smw(
ActorDiscrete(c.observe_dim,
c.action_dim).type(dtype).to(device),
device,
device,
)
actor_t = smw(
ActorDiscrete(c.observe_dim,
c.action_dim).type(dtype).to(device),
device,
device,
)
critic = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device,
device)
critic_t = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device,
device)
servers = model_server_helper(model_num=2)
world = get_world()
# process 0 and 1 will be workers, and 2 will be trainer
apex_group = world.create_rpc_group("worker", ["0", "1", "2"])
ddpg_apex = DDPGApex(
actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
apex_group,
servers,
replay_device="cpu",
replay_size=c.replay_size,
)
return ddpg_apex
########################################################################
# Test for DDPGApex contiguous domain acting
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_contiguous_act(_, device, dtype):
c = TestDDPGApex.c
ddpg_apex = TestDDPGApex.ddpg_apex(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
ddpg_apex.act({"state": state})
ddpg_apex.act({"state": state}, use_target=True)
ddpg_apex.act_with_noise({"state": state},
noise_param=(0, 1.0),
mode="uniform")
ddpg_apex.act_with_noise({"state": state},
noise_param=(0, 1.0),
mode="uniform",
use_target=True)
return True
########################################################################
# Test for DDPGApex discrete domain acting
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_discrete_act(_, device, dtype):
c = TestDDPGApex.c
c.device = gpu
ddpg_apex = TestDDPGApex.ddpg_apex(device, dtype, discrete=True)
state = t.zeros([1, c.observe_dim], dtype=dtype)
ddpg_apex.act_discrete({"state": state})
ddpg_apex.act_discrete({"state": state}, use_target=True)
ddpg_apex.act_discrete_with_noise({"state": state})
ddpg_apex.act_discrete_with_noise({"state": state}, use_target=True)
return True
########################################################################
# Test for DDPGApex criticizing
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test__criticize(_, device, dtype):
c = TestDDPGApex.c
c.device = gpu
ddpg_apex = TestDDPGApex.ddpg_apex(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, c.action_dim], dtype=dtype)
ddpg_apex._criticize({"state": state}, {"action": action})
ddpg_apex._criticize({"state": state}, {"action": action},
use_target=True)
return True
########################################################################
# Test for DDPGApex storage
########################################################################
# Skipped, it is the same as DDPG
########################################################################
# Test for DDPGApex update
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_update(rank, device, dtype):
c = TestDDPGApex.c
c.device = gpu
ddpg_apex = TestDDPGApex.ddpg_apex(device, dtype)
old_state = state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, c.action_dim], dtype=dtype)
if rank in (0, 1):
ddpg_apex.store_transition({
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"terminal": False,
})
sleep(5)
ddpg_apex.manual_sync()
if rank == 2:
sleep(2)
ddpg_apex.update(
update_value=True,
update_policy=True,
update_target=True,
concatenate_samples=True,
)
return True
########################################################################
# Test for DDPGApex save & load
########################################################################
# Skipped, it is the same as DDPG
########################################################################
# Test for DDPGApex lr_scheduler
########################################################################
# Skipped, it is the same as DDPG
########################################################################
# Test for DDPGApex config & init
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=180)
@WorldTestBase.setup_world
def test_config_init(rank):
c = TestDDPGApex.c
config = DDPGApex.generate_config({})
config["frame_config"]["models"] = [
"Actor", "Actor", "Critic", "Critic"
]
config["frame_config"][
"model_kwargs"] = [{
"state_dim": c.observe_dim,
"action_dim": c.action_dim,
"action_range": c.action_range,
}] * 2 + [{
"state_dim": c.observe_dim,
"action_dim": c.action_dim
}] * 2
ddpg_apex = DDPGApex.init_from_config(config)
old_state = state = t.zeros([1, c.observe_dim], dtype=t.float32)
action = t.zeros([1, c.action_dim], dtype=t.float32)
if rank in (1, 2):
ddpg_apex.store_transition({
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"terminal": False,
})
sleep(5)
ddpg_apex.manual_sync()
if rank == 0:
sleep(2)
ddpg_apex.update()
return True
########################################################################
# Test for DDPGApex full training.
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=1800)
@WorldTestBase.setup_world
def test_full_train(rank):
c = TestDDPGApex.c
ddpg_apex = TestDDPGApex.ddpg_apex("cpu", t.float32, discrete=True)
# perform manual syncing to decrease the number of rpc calls
ddpg_apex.set_sync(False)
# begin training
episode, step = Counter(), Counter()
avg_step = Smooth()
reward_fulfilled = Counter()
smoother = Smooth()
terminal = False
env = c.env
world = get_world()
all_group = world.create_rpc_group("all", ["0", "1", "2"])
all_group.pair(f"{rank}_running", True)
default_logger.info(f"{rank}, pid {os.getpid()}")
if rank == 0:
all_group.pair("episode", episode)
if rank in (0, 1):
while episode < c.max_episodes:
# wait for trainer to keep up
sleep(0.2)
episode.count()
# batch size = 1
total_reward = 0
state = t.tensor(env.reset(), dtype=t.float32)
ddpg_apex.manual_sync()
while not terminal and step <= c.max_steps:
step.count()
with t.no_grad():
old_state = state
action, probs = ddpg_apex.act_discrete_with_noise(
{"state": old_state.unsqueeze(0)})
state, reward, terminal, _ = env.step(
action.cpu().item())
state = t.tensor(state, dtype=t.float32).flatten()
total_reward += float(reward)
ddpg_apex.store_transition({
"state": {
"state": old_state.unsqueeze(0)
},
"action": {
"action": probs
},
"next_state": {
"state": state.unsqueeze(0)
},
"reward":
float(reward),
"terminal":
terminal or step == c.max_steps,
})
smoother.update(total_reward)
avg_step.update(step.get())
step.reset()
terminal = False
default_logger.info("Process {} Episode {} "
"total reward={:.2f}".format(
rank, episode, smoother.value))
if smoother.value > c.solved_reward:
reward_fulfilled.count()
if reward_fulfilled >= c.solved_repeat:
default_logger.info("Environment solved!")
all_group.unpair(f"{rank}_running")
while all_group.is_paired(
"0_running") or all_group.is_paired(
"1_running"):
# wait for all workers to join
sleep(1)
# wait for trainer
sleep(5)
return True
else:
reward_fulfilled.reset()
else:
# wait for some samples
while ddpg_apex.replay_buffer.all_size() < 500:
sleep(0.1)
while all_group.is_paired("0_running") or all_group.is_paired(
"1_running"):
ddpg_apex.update()
default_logger.info(f"Updated")
return True
raise RuntimeError("DDPG-Apex Training failed.")
class TestARS:
# configs and definitions
# Cartpole-v0 can be solved:
# within 200 episodes, using single layer Actor
# within 400 episodes, using double layer Actor
# However, ARS fails to deal with pendulum v0:
# Actor((st, 16)->(16, a)), noise_std=0.01, lr=0.05, rollout=9, optim=Adam)
# reaches mean score = -700 at 10000 episodes
# Actor((st, a)), noise_std=0.01, lr=0.05, rollout=9, optim=Adam)
# reaches mean score = -1100 at 15000 episodes
# and Adam optimizer is better than SGD
disable_view_window()
c = Config()
c.env_name = "CartPole-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 4
c.action_num = 2
c.max_episodes = 1000
c.max_steps = 200
c.solved_reward = 150
c.solved_repeat = 5
@staticmethod
def ars(device, dtype):
c = TestARS.c
actor = smw(
ActorDiscrete(c.observe_dim, c.action_num).type(dtype).to(device),
device,
device,
)
servers = model_server_helper(model_num=1)
world = get_world()
ars_group = world.create_rpc_group("ars", ["0", "1", "2"])
ars = ARS(
actor,
t.optim.SGD,
ars_group,
servers,
noise_std_dev=0.1,
learning_rate=0.1,
noise_size=1000000,
rollout_num=6,
used_rollout_num=6,
normalize_state=True,
)
return ars
@staticmethod
def ars_lr(device, dtype):
c = TestARS.c
actor = smw(
ActorDiscrete(c.observe_dim, c.action_num).type(dtype).to(device),
device,
device,
)
lr_func = gen_learning_rate_func([(0, 1e-3), (200000, 3e-4)],
logger=default_logger)
servers = model_server_helper(model_num=1)
world = get_world()
ars_group = world.create_rpc_group("ars", ["0", "1", "2"])
ars = ARS(
actor,
t.optim.SGD,
ars_group,
servers,
noise_size=1000000,
lr_scheduler=LambdaLR,
lr_scheduler_args=((lr_func, ), ),
)
return ars
########################################################################
# Test for ARS acting
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_act(_, device, dtype):
c = TestARS.c
ars = TestARS.ars(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
ars.act({"state": state}, "original")
ars.act({"state": state}, ars.get_actor_types()[0])
with pytest.raises(ValueError):
ars.act({"state": state}, "some_invalid_actor_type")
return True
########################################################################
# Test for ARS storage
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_store_reward(_, device, dtype):
ars = TestARS.ars(device, dtype)
ars.store_reward(0.0, ars.get_actor_types()[0])
with pytest.raises(ValueError):
ars.store_reward(1.0, "some_invalid_actor_type")
return True
########################################################################
# Test for ARS update
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_update(_, device, dtype):
c = TestARS.c
ars = TestARS.ars(device, dtype)
for at in ars.get_actor_types():
# get action will cause filters to initialize
_action = ars.act(
{"state": t.zeros([1, c.observe_dim], dtype=dtype)}, at)
if at.startswith("neg"):
ars.store_reward(1.0, at)
else:
ars.store_reward(0.0, at)
ars.update()
return True
########################################################################
# Test for ARS save & load
########################################################################
# Skipped, it is the same as base
########################################################################
# Test for ARS lr_scheduler
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@WorldTestBase.setup_world
def test_lr_scheduler(_, device, dtype):
ars = TestARS.ars_lr(device, dtype)
ars.update_lr_scheduler()
return True
########################################################################
# Test for ARS config & init
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=180)
@WorldTestBase.setup_world
def test_config_init(_):
c = TestARS.c
config = ARS.generate_config({})
config["frame_config"]["models"] = ["ActorDiscrete"]
config["frame_config"]["model_kwargs"] = [{
"state_dim": c.observe_dim,
"action_dim": c.action_num
}]
ars = ARS.init_from_config(config)
for at in ars.get_actor_types():
# get action will cause filters to initialize
_action = ars.act(
{"state": t.zeros([1, c.observe_dim], dtype=t.float32)}, at)
if at.startswith("neg"):
ars.store_reward(1.0, at)
else:
ars.store_reward(0.0, at)
ars.update()
return True
########################################################################
# Test for ARS full training.
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=1800)
@WorldTestBase.setup_world
def test_full_train(rank):
c = TestARS.c
ars = TestARS.ars("cpu", t.float32)
# begin training
episode, step = Counter(), Counter()
reward_fulfilled = Counter()
smoother = Smooth()
terminal = False
env = c.env
# for cpu usage viewing
default_logger.info(f"{rank}, pid {os.getpid()}")
while episode < c.max_episodes:
episode.count()
all_reward = 0
for at in ars.get_actor_types():
total_reward = 0
# batch size = 1
state = t.tensor(env.reset(), dtype=t.float32)
while not terminal and step <= c.max_steps:
step.count()
with t.no_grad():
# agent model inference
action = ars.act({"state": state.unsqueeze(0)}, at)
state, reward, terminal, __ = env.step(action)
state = t.tensor(state, dtype=t.float32)
total_reward += float(reward)
step.reset()
terminal = False
ars.store_reward(total_reward, at)
all_reward += total_reward
# update
ars.update()
smoother.update(all_reward / len(ars.get_actor_types()))
default_logger.info(
f"Process {rank} Episode {episode} total reward={smoother.value:.2f}"
)
if smoother.value > c.solved_reward:
reward_fulfilled.count()
if reward_fulfilled >= c.solved_repeat:
default_logger.info("Environment solved!")
raise SafeExit
else:
reward_fulfilled.reset()
raise RuntimeError("ARS Training failed.")
def train_config(self, pytestconfig):
disable_view_window()
c = Config()
c.env_name = "Pendulum-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 3
c.action_dim = 1
c.action_range = 2
c.max_episodes = 1000
c.max_steps = 200
c.replay_size = 100000
c.solved_reward = -150
c.solved_repeat = 5
c.device = "cpu"
return c
class TestDQNApex:
# configs and definitions
disable_view_window()
c = Config()
# Note: online policy algorithms such as PPO and A2C does not
# work well in Pendulum (reason unknown)
# and MountainCarContinuous (sparse returns)
c.env_name = "CartPole-v0"
c.env = unwrap_time_limit(gym.make(c.env_name))
c.observe_dim = 4
c.action_num = 2
c.max_episodes = 20000
c.max_steps = 200
c.replay_size = 100000
c.solved_reward = 150
c.solved_repeat = 5
@staticmethod
def dqn_apex(device, dtype):
c = TestDQNApex.c
q_net = smw(
QNet(c.observe_dim, c.action_num).type(dtype).to(device), device, device
)
q_net_t = smw(
QNet(c.observe_dim, c.action_num).type(dtype).to(device), device, device
)
servers = model_server_helper(model_num=1)
world = get_world()
# process 0 and 1 will be workers, and 2 will be trainer
apex_group = world.create_rpc_group("apex", ["0", "1", "2"])
dqn_apex = DQNApex(
q_net,
q_net_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
apex_group,
servers,
replay_device="cpu",
replay_size=c.replay_size,
)
return dqn_apex
########################################################################
# Test for DQNApex acting
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@setup_world
def test_act(_, device, dtype):
c = TestDQNApex.c
dqn_apex = TestDQNApex.dqn_apex(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
dqn_apex.act_discrete({"state": state})
dqn_apex.act_discrete({"state": state}, True)
dqn_apex.act_discrete_with_noise({"state": state})
dqn_apex.act_discrete_with_noise({"state": state}, True)
return True
########################################################################
# Test for DQNApex criticizing
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@setup_world
def test_criticize(_, device, dtype):
c = TestDQNApex.c
dqn_apex = TestDQNApex.dqn_apex(device, dtype)
state = t.zeros([1, c.observe_dim], dtype=dtype)
dqn_apex._criticize({"state": state})
dqn_apex._criticize({"state": state}, True)
return True
########################################################################
# Test for DQNApex storage
########################################################################
# Skipped, it is the same as DQN
########################################################################
# Test for DQNApex update
########################################################################
@staticmethod
@run_multi(
expected_results=[True, True, True],
pass_through=["device", "dtype"],
timeout=180,
)
@setup_world
def test_update(rank, device, dtype):
c = TestDQNApex.c
dqn_apex = TestDQNApex.dqn_apex(device, dtype)
old_state = state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
if rank in (0, 1):
dqn_apex.store_episode(
[
{
"state": {"state": old_state},
"action": {"action": action},
"next_state": {"state": state},
"reward": 0,
"terminal": False,
}
for _ in range(3)
]
)
dqn_apex.manual_sync()
if rank == 2:
sleep(2)
dqn_apex.update()
return True
########################################################################
# Test for DQNApex save & load
########################################################################
# Skipped, it is the same as DQN
########################################################################
# Test for DQNApex lr_scheduler
########################################################################
# Skipped, it is the same as DQN
########################################################################
# Test for DQNApex config & init
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=180)
@setup_world
def test_config_init(rank):
c = TestDQNApex.c
config = DQNApex.generate_config({})
config["frame_config"]["models"] = ["QNet", "QNet"]
config["frame_config"]["model_kwargs"] = [
{"state_dim": c.observe_dim, "action_num": c.action_num}
] * 2
dqn_apex = DQNApex.init_from_config(config)
old_state = state = t.zeros([1, c.observe_dim], dtype=t.float32)
action = t.zeros([1, 1], dtype=t.int)
if rank in (1, 2):
dqn_apex.store_episode(
[
{
"state": {"state": old_state},
"action": {"action": action},
"next_state": {"state": state},
"reward": 0,
"terminal": False,
}
for _ in range(3)
]
)
dqn_apex.manual_sync()
if rank == 0:
sleep(2)
dqn_apex.update(
update_value=True, update_target=True, concatenate_samples=True
)
return True
########################################################################
# Test for DQNApex full training.
########################################################################
@staticmethod
@run_multi(expected_results=[True, True, True], timeout=1800)
@setup_world
def test_full_train(rank):
training_group = get_world().create_rpc_group("training", ["0", "1", "2"])
c = TestDQNApex.c
dqn_apex = TestDQNApex.dqn_apex("cpu", t.float32)
# perform manual syncing to decrease the number of rpc calls
dqn_apex.set_sync(False)
# begin training
episode, step = Counter(), Counter()
reward_fulfilled = Counter()
smoother = Smooth()
terminal = False
env = c.env
env.seed(rank)
# make sure all things are initialized.
training_group.barrier()
# for cpu usage viewing
default_logger.info(f"{rank}, pid {os.getpid()}")
while episode < c.max_episodes:
episode.count()
if rank in (0, 1):
# batch size = 1
total_reward = 0
state = t.tensor(env.reset(), dtype=t.float32)
dqn_apex.manual_sync()
tmp_observations = []
while not terminal and step <= c.max_steps:
step.count()
with t.no_grad():
old_state = state
# agent model inference
action = dqn_apex.act_discrete_with_noise(
{"state": old_state.unsqueeze(0)}
)
state, reward, terminal, _ = env.step(action.item())
state = t.tensor(state, dtype=t.float32).flatten()
total_reward += float(reward)
tmp_observations.append(
{
"state": {"state": old_state.unsqueeze(0)},
"action": {"action": action},
"next_state": {"state": state.unsqueeze(0)},
"reward": float(reward),
"terminal": terminal or step == c.max_steps,
}
)
dqn_apex.store_episode(tmp_observations)
smoother.update(total_reward)
step.reset()
terminal = False
default_logger.info(
"Process {} Episode {} total reward={:.2f}".format(
rank, episode, smoother.value
)
)
if smoother.value > c.solved_reward:
reward_fulfilled.count()
if reward_fulfilled >= c.solved_repeat:
default_logger.info("Environment solved!")
try:
training_group.pair(f"solved", True)
except KeyError:
# already solved in another process
pass
else:
reward_fulfilled.reset()
else:
# wait for some samples
if episode.get() > 200:
for _ in range(100):
dqn_apex.update()
default_logger.info("Updated 100 times.")
training_group.barrier()
if training_group.is_paired("solved"):
return True
raise RuntimeError("DQN-Apex Training failed.")