def dqn_lr(self, train_config):
# not used for training, only used for testing apis
c = train_config
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)
lr_func = gen_learning_rate_func([(0, 1e-3), (200000, 3e-4)],
logger=logger)
with pytest.raises(TypeError, match="missing .+ positional argument"):
_ = DQN(q_net,
q_net_t,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
replay_device=c.device,
replay_size=c.replay_size,
lr_scheduler=LambdaLR)
dqn = DQN(q_net,
q_net_t,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
replay_device=c.device,
replay_size=c.replay_size,
lr_scheduler=LambdaLR,
lr_scheduler_args=((lr_func, ), ))
return dqn
def test_config_init(self, train_config):
c = train_config
config = DQN.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 = DQN.init_from_config(config)
old_state = state = t.zeros([1, c.observe_dim], dtype=t.float32)
action = t.zeros([1, 1], dtype=t.int)
dqn.store_episode([{
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"terminal": False,
} for _ in range(3)])
dqn.update()
def dqn_train(self, train_config, request):
c = train_config
# cpu is faster for testing full training.
q_net = smw(QNet(c.observe_dim, c.action_num), "cpu", "cpu")
q_net_t = smw(QNet(c.observe_dim, c.action_num), "cpu", "cpu")
dqn = DQN(q_net, q_net_t,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
replay_device="cpu",
replay_size=c.replay_size,
mode=request.param)
return dqn
def test_mode(self, train_config, device, dtype):
c = train_config
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)
with pytest.raises(ValueError, match="Unknown DQN mode"):
_ = DQN(
q_net,
q_net_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
replay_device="cpu",
replay_size=c.replay_size,
mode="invalid_mode",
)
with pytest.raises(ValueError, match="Unknown DQN mode"):
dqn = DQN(
q_net,
q_net_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
replay_device="cpu",
replay_size=c.replay_size,
mode="double",
)
old_state = state = t.zeros([1, c.observe_dim], dtype=dtype)
action = t.zeros([1, 1], dtype=t.int)
dqn.store_episode([{
"state": {
"state": old_state
},
"action": {
"action": action
},
"next_state": {
"state": state
},
"reward": 0,
"terminal": False,
} for _ in range(3)])
dqn.mode = "invalid_mode"
dqn.update(update_value=True,
update_target=True,
concatenate_samples=True)
def dqn(self, train_config, device, dtype, request):
c = train_config
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)
dqn = DQN(q_net, q_net_t,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
replay_device="cpu",
replay_size=c.replay_size,
mode=request.param)
return dqn
def dqn_vis(self, train_config, device, dtype, tmpdir, request):
c = train_config
tmp_dir = tmpdir.make_numbered_dir()
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)
dqn = DQN(q_net, q_net_t,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
replay_device="cpu",
replay_size=c.replay_size,
mode=request.param,
visualize=True,
visualize_dir=str(tmp_dir))
return dqn
