def test_clipping_per_neuron(self, ctrnn_config, brain_param_identity, box2d):
ctrnn_config = evolve(ctrnn_config, optimize_state_boundaries="per_neuron")
bp = evolve(brain_param_identity, clip_max=np.array([2, 3]), clip_min=np.array([-4, -5]))
ContinuousTimeRNN.generate_and_set_class_state(config=ctrnn_config, input_space=box2d,
output_space=box2d, )
brain = ContinuousTimeRNN(input_space=box2d, output_space=box2d,
individual=self.param_to_genom(bp), config=ctrnn_config)
ob = np.array([1, 1])
brain.step(ob * 100000)
assert np.allclose(brain.y, bp.clip_max)
brain.step(ob * -100000)
assert np.allclose(brain.y, bp.clip_min)
def test_step(self, ctrnn_config, brain_param_identity, box2d):
ctrnn_config = evolve(ctrnn_config, set_principle_diagonal_elements_of_W_negative=False)
bp = brain_param_identity
ContinuousTimeRNN.generate_and_set_class_state(config=ctrnn_config, input_space=box2d,
output_space=Box(-1, 1, shape=[2]), )
brain = ContinuousTimeRNN(input_space=box2d, output_space=box2d, individual=self.param_to_genom(bp),
config=ctrnn_config)
brain.delta_t = 1.0
ob = np.array([1, 1])
assert np.allclose(brain.y, np.zeros([2, 2]))
res = brain.step(ob)
# due to identity matrices after one iteration the internal state is now exactly the observersion
assert np.allclose(brain.y, ob)
# due to identity matrices after one iteration the output is just the input, but with tanh.
assert np.allclose(res, np.tanh(ob))
brain.step(ob)
assert np.allclose(brain.y, np.tanh(ob) + ob + ob)
def test_clipping_legacy(self, ctrnn_config, brain_param_identity, box2d):
bp = brain_param_identity
ContinuousTimeRNN.generate_and_set_class_state(config=ctrnn_config, input_space=box2d,
output_space=Box(-1, 1, shape=[2]), )
brain = ContinuousTimeRNN(input_space=box2d, output_space=box2d, individual=self.param_to_genom(bp),
config=ctrnn_config)
ob = np.array([1, 1])
res = brain.step(ob * 1000)
# due to tanh the maximum output is 1.0
assert np.allclose(res, np.ones(2))
# with legacy-clipping everything is clipped to the lowest max-value, which is 10 in this genome
assert np.allclose(brain.y, np.ones(2) * 10)
output_space=experiment.output_space,
individual=ind,
config=cfg_exp.brain)
ob = env.reset()
transformed_ob = transform(ob,
coming_from_space=experiment.input_space,
is_brain_input=True)
env.unwrapped.input_data = [0, 1, 0, 1, 1]
env.unwrapped.target = env.unwrapped.target_from_input_data(
env.unwrapped.input_data)
env.render()
done = False
fitness_current = 0
while not done:
brain_output = brain.step(transformed_ob)
action = transform(brain_output,
coming_from_space=experiment.output_space,
is_brain_input=False)
print("act: " + str(action))
ob, rew, done, info = env.step(action)
transformed_ob = transform(ob,
coming_from_space=experiment.input_space,
is_brain_input=True)
fitness_current += rew
if rew < 0:
print("error")
env.render()
env.render()
print('score: ' + str(fitness_current))
env = experiment.env_template
for i in range(1):
brain = ContinuousTimeRNN(input_space=experiment.input_space,
output_space=experiment.output_space,
individual=ind,
config=cfg_exp.brain)
ob = env.reset()
env.unwrapped.input_data = [0, 1, 0, 1, 1]
env.unwrapped.target = env.unwrapped.target_from_input_data(
env.unwrapped.input_data)
env.render()
done = False
fitness_current = 0
while not done:
brain_output = brain.step(ob)
action = output_to_action(brain_output, experiment.output_space)
print("act: " + str(action))
ob, rew, done, info = env.step(action)
fitness_current += rew
if rew < 0:
print("error")
env.render()
env.render()
print('score: ' + str(fitness_current))
# todo: use brain_vis to visualize this
t = threading.Thread(target=experiment.visualize,
args=[[ind],
BrainVisualizerHandler(), 2, False, False])