def test_continuous_action_prediction(behavior_spec: BehaviorSpec, seed: int) -> None:
np.random.seed(seed)
torch.manual_seed(seed)
curiosity_settings = CuriositySettings(32, 0.1)
curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
buffer = create_agent_buffer(behavior_spec, 5)
for _ in range(200):
curiosity_rp.update(buffer)
prediction = curiosity_rp._network.predict_action(buffer)[0]
target = torch.tensor(buffer["actions"][0])
error = torch.mean((prediction - target) ** 2).item()
assert error < 0.001
def test_next_state_prediction(behavior_spec: BehaviorSpec, seed: int) -> None:
np.random.seed(seed)
torch.manual_seed(seed)
curiosity_settings = CuriositySettings(32, 0.1)
curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
buffer = create_agent_buffer(behavior_spec, 5)
for _ in range(100):
curiosity_rp.update(buffer)
prediction = curiosity_rp._network.predict_next_state(buffer)[0]
target = curiosity_rp._network.get_next_state(buffer)[0]
error = float(ModelUtils.to_numpy(torch.mean((prediction - target) ** 2)))
assert error < 0.001
def test_reward_decreases(behavior_spec: BehaviorSpec, seed: int) -> None:
np.random.seed(seed)
torch.manual_seed(seed)
curiosity_settings = CuriositySettings(32, 0.01)
curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
buffer = create_agent_buffer(behavior_spec, 5)
curiosity_rp.update(buffer)
reward_old = curiosity_rp.evaluate(buffer)[0]
for _ in range(20):
curiosity_rp.update(buffer)
reward_new = curiosity_rp.evaluate(buffer)[0]
assert reward_new < reward_old
def test_construction(behavior_spec: BehaviorSpec) -> None:
curiosity_settings = CuriositySettings(32, 0.01)
curiosity_settings.strength = 0.1
curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
assert curiosity_rp.strength == 0.1
assert curiosity_rp.name == "Curiosity"
