def npi_program_interface(self, env, program: Program, arguments: IntegerArguments, depth=0):
if self.max_depth < depth or self.max_step < self.steps:
raise StopIteration()
self.model.enter_function()
result = StepOutput(0, None, None)
while result.r < self.alpha:
self.steps += 1
if self.max_step < self.steps:
raise StopIteration()
env_observation = env.get_observation()
result = self.model.step(env_observation, program, arguments.copy())
if self.recording:
self.step_list.append(StepInOut(StepInput(env_observation, program, arguments.copy()), result))
self.display_information(program, arguments, result, depth)
if program.output_to_env:
program.do(env, arguments.copy())
self.display_env(env)
else:
if result.program: # modify original algorithm
self.npi_program_interface(env, result.program, result.arguments, depth=depth+1)
self.model.exit_function()
def npi_program_interface(self, env, program: Program, arguments: IntegerArguments, depth=0):
if self.max_depth < depth or self.max_step < self.steps:
raise StopIteration()
self.model.enter_function()
result = StepOutput(0, None, None)
while result.r < self.alpha:
self.steps += 1
if self.max_step < self.steps:
raise StopIteration()
env_observation = env.get_observation()
result = self.model.step(env_observation, program, arguments.copy())
if self.recording:
self.step_list.append(StepInOut(StepInput(env_observation, program, arguments.copy()), result))
self.display_information(program, arguments, result, depth)
if program.output_to_env:
program.do(env, arguments.copy())
self.display_env(env)
else:
if result.program: # modify original algorithm
self.npi_program_interface(env, result.program, result.arguments, depth=depth+1)
self.model.exit_function()
def npi_program_interface(self,
env,
program: Program,
arguments: IntegerArguments,
depth=0):
if self.max_depth < depth or self.max_step < self.steps:
if self.max_step < self.steps:
print("stop iteration becasue there are too many steps")
self.terminal.add_log("stop iteration becasue it's too deep")
if self.max_depth < depth:
print("stop iteration becasue there are too many steps")
self.terminal.add_log("stop iteration becasue it's too deep")
raise StopIteration()
self.model.enter_function()
# self.terminal.add_log("enter function")
result = StepOutput(0, None, None)
# self.terminal.add_log(result)
while result.r < self.alpha:
self.terminal.add_log("in a function")
self.steps += 1
if self.max_step < self.steps:
print("stop iteration becasue there are too many steps")
self.terminal.add_log(
"stop iteration becasue there are too many steps")
raise StopIteration()
env_observation = env.get_observation()
#self.terminal.add_log(env_observation)
# run our model for one step
# result is a StepOutput
result = self.model.step(env_observation, program,
arguments.copy())
self.terminal.add_log("after excuting funciton")
# self.terminal.add_log(result)
# what's the intuition of recording
if self.recording:
self.step_list.append(
StepInOut(
StepInput(env_observation, program, arguments.copy()),
result))
self.display_information(program, arguments, result, depth)
if program.output_to_env:
# self.terminal.add_log("output_to_env")
program.do(env, arguments.copy())
self.display_env(env)
else:
self.terminal.add_log(str(program))
if result.program: # modify original algorithm
# self.terminal.add_log("execute sub_program")
self.npi_program_interface(env,
result.program,
result.arguments,
depth=depth + 1)
self.terminal.add_log("exit funtion")
self.model.exit_function()
