def test_prioritized_dqn_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
replay_memory = PrioritizedReplayMemory(50,
500,
alpha=.6,
beta=LinearParameter(
.4,
threshold_value=1,
n=500 // 5))
params = dict(batch_size=50,
initial_replay_size=50,
max_replay_size=500,
target_update_frequency=50,
replay_memory=replay_memory)
agent_save = learn(DQN, params)
agent_save.save(agent_path, full_save=True)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_copdac_q_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
agent_save = learn_copdac_q()
agent_save.save(agent_path)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_PGPE_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
agent_save = learn(PGPE, optimizer=AdaptiveOptimizer(1.5))
agent_save.save(agent_path)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_a2c_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(datetime.now().strftime("%H%M%S%f"))
agent_save = learn_a2c()
agent_save.save(agent_path, full_save=True)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
print('checking ', att)
print(save_attr, load_attr)
tu.assert_eq(save_attr, load_attr)
def test_fqi_boosted_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
params = dict(n_iterations=10)
agent_save, _ = learn(BoostedFQI, params)
agent_save.save(agent_path)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_GPOMDP_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
params = dict(optimizer=AdaptiveOptimizer(eps=.01))
agent_save = learn(GPOMDP, params)
agent_save.save(agent_path)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_noisy_dqn_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
params = dict(batch_size=50,
initial_replay_size=50,
max_replay_size=5000,
target_update_frequency=50)
agent_save = learn(NoisyDQN, params)
agent_save.save(agent_path, full_save=True)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_q_learning_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
pi, mdp, _ = initialize()
agent_save = QLearning(mdp.info, pi, Parameter(.5))
core = Core(agent_save, mdp)
# Train
core.learn(n_steps=100, n_steps_per_fit=1, quiet=True)
agent_save.save(agent_path)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_rainbow_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
params = dict(batch_size=50,
initial_replay_size=50,
max_replay_size=500,
target_update_frequency=50,
n_steps_return=1,
alpha_coeff=.6,
beta=LinearParameter(.4, threshold_value=1, n=500 // 5))
agent_save = learn(Rainbow, params)
agent_save.save(agent_path, full_save=True)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_TRPO_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
params = dict(ent_coeff=0.0,
max_kl=.001,
lam=.98,
n_epochs_line_search=10,
n_epochs_cg=10,
cg_damping=1e-2,
cg_residual_tol=1e-10)
agent_save = learn(TRPO, params)
agent_save.save(agent_path)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_sarsa_lambda_continuous_linear_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
pi, _, mdp_continuous = initialize()
mdp_continuous.seed(1)
n_tilings = 1
tilings = Tiles.generate(n_tilings, [2, 2],
mdp_continuous.info.observation_space.low,
mdp_continuous.info.observation_space.high)
features = Features(tilings=tilings)
approximator_params = dict(
input_shape=(features.size, ),
output_shape=(mdp_continuous.info.action_space.n, ),
n_actions=mdp_continuous.info.action_space.n)
agent_save = SARSALambdaContinuous(mdp_continuous.info,
pi,
LinearApproximator,
Parameter(.1),
.9,
features=features,
approximator_params=approximator_params)
core = Core(agent_save, mdp_continuous)
# Train
core.learn(n_steps=100, n_steps_per_fit=1, quiet=True)
agent_save.save(agent_path)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
def test_PPO_save(tmpdir):
agent_path = tmpdir / 'agent_{}'.format(
datetime.now().strftime("%H%M%S%f"))
params = dict(actor_optimizer={
'class': optim.Adam,
'params': {
'lr': 3e-4
}
},
n_epochs_policy=4,
batch_size=64,
eps_ppo=.2,
lam=.95)
agent_save = learn(PPO, params)
agent_save.save(agent_path)
agent_load = Agent.load(agent_path)
for att, method in vars(agent_save).items():
save_attr = getattr(agent_save, att)
load_attr = getattr(agent_load, att)
tu.assert_eq(save_attr, load_attr)
