<gh_stars>1-10
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "<NAME>"

import numpy
from neodroid.models import Configuration

import neodroid.wrappers.curriculum_wrapper as neo

random_motion_horizon = 5
_memory = []
_sampled_initial_state_values = []


def get_goal_configuration(environment):
    if environment:
        # goal_pos_x = environment.description.configurable('GoalTransformX').observation
        # goal_pos_z = environment.description.configurable('GoalTransformZ').observation
        # return goal_pos_x,goal_pos_z
        return environment.description.configurable("GoalPosition").configurable_value


def main():
    _environment = neo.make("maze", connect_to_running=True)
    _environment.seed(42)

    goal_pos = get_goal_configuration(_environment)

    initial_configuration = [
        Configuration("ActorPositionX", goal_pos[0]),
        Configuration("ActorPositionY", goal_pos[1]),
        Configuration("ActorPositionZ", goal_pos[2]),
    ]
    _memory.extend(
        _environment.generate_initial_states_from_configuration(initial_configuration)
    )

    for i in range(300):
        state = sample_initial_state(_memory)
        if not _environment.is_connected:
            break

        if i % 20 == 19:
            _memory.extend(_environment.generate_initial_states_from_state(state))

        _environment.configure(state=state)

        terminated = False
        while not terminated:
            actions = _environment.action_space.sample()
            observations, reward, terminated, info = _environment.act(actions)
            if terminated:
                print("Interrupted", reward)
                break

    _environment.close()


def sample_initial_state(memory):
    idx = numpy.random.randint(0, len(memory))
    return memory[idx]


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "<NAME>"

import numpy
from neodroid.models import Configuration

import neodroid.wrappers.curriculum_wrapper as neo

random_motion_horizon = 5
_memory = []
_sampled_initial_state_values = []


def get_goal_configuration(environment):
    if environment:
        # goal_pos_x = environment.description.configurable('GoalTransformX').observation
        # goal_pos_z = environment.description.configurable('GoalTransformZ').observation
        # return goal_pos_x,goal_pos_z
        return environment.description.configurable("GoalPosition").configurable_value


def main():
    _environment = neo.make("maze", connect_to_running=True)
    _environment.seed(42)

    goal_pos = get_goal_configuration(_environment)

    initial_configuration = [
        Configuration("ActorPositionX", goal_pos[0]),
        Configuration("ActorPositionY", goal_pos[1]),
        Configuration("ActorPositionZ", goal_pos[2]),
    ]
    _memory.extend(
        _environment.generate_initial_states_from_configuration(initial_configuration)
    )

    for i in range(300):
        state = sample_initial_state(_memory)
        if not _environment.is_connected:
            break

        if i % 20 == 19:
            _memory.extend(_environment.generate_initial_states_from_state(state))

        _environment.configure(state=state)

        terminated = False
        while not terminated:
            actions = _environment.action_space.sample()
            observations, reward, terminated, info = _environment.act(actions)
            if terminated:
                print("Interrupted", reward)
                break

    _environment.close()


def sample_initial_state(memory):
    idx = numpy.random.randint(0, len(memory))
    return memory[idx]


if __name__ == "__main__":
    main()
#!/usr/bin/env python3 # -*- coding: utf-8 -*- # goal_pos_x = environment.description.configurable('GoalTransformX').observation # goal_pos_z = environment.description.configurable('GoalTransformZ').observation # return goal_pos_x,goal_pos_z