Gym_CPU/agent/agent.py

from dataclasses import Field
import logging
from typing import Mapping

import numpy as np
from utils.math_ops import MathOps
from world.commons.field import FIFAField, HLAdultField
from world.commons.play_mode import PlayModeEnum, PlayModeGroupEnum


logger = logging.getLogger()


class Agent:
    """
    Responsible for deciding what the agent should do at each moment.

    This class is called every simulation step to update the agent's behavior
    based on the current state of the world and game conditions.
    """

    BEAM_POSES: Mapping[type[Field], Mapping[int, tuple[float, float, float]]] ={
        FIFAField: {
            1: (2.1, 0, 0),
            2: (22.0, 12.0, 0),
            3: (22.0, 4.0, 0),
            4: (22.0, -4.0, 0),
            5: (22.0, -12.0, 0),
            6: (15.0, 0.0, 0),
            7: (4.0, 16.0, 0),
            8: (11.0, 6.0, 0),
            9: (11.0, -6.0, 0),
            10: (4.0, -16.0, 0),
            11: (7.0, 0.0, 0),
        },
        HLAdultField: {
            1: (7.0, 0.0, 0),
            2: (2.0, -1.5, 0),
            3: (2.0, 1.5, 0),
        }
    }

    def __init__(self, agent):
        """
        Creates a new DecisionMaker linked to the given agent.

        Args:
            agent: The main agent that owns this DecisionMaker.
        """
        from agent.base_agent import Base_Agent  # type hinting

        self.agent: Base_Agent = agent
        self.is_getting_up: bool = False

    def update_current_behavior(self) -> None:
        """
        Chooses what the agent should do in the current step.

        This function checks the game state and decides which behavior
        or skill should be executed next.
        """

        if self.agent.world.playmode is PlayModeEnum.GAME_OVER:
            return

        if self.agent.world.playmode_group in (
            PlayModeGroupEnum.ACTIVE_BEAM,
            PlayModeGroupEnum.PASSIVE_BEAM,
        ):
            self.agent.server.commit_beam(
                pos2d=self.BEAM_POSES[type(self.agent.world.field)][self.agent.world.number][:2],
                rotation=self.BEAM_POSES[type(self.agent.world.field)][self.agent.world.number][2],
            )

        if self.is_getting_up or self.agent.skills_manager.is_ready(skill_name="GetUp"):
            self.is_getting_up = not self.agent.skills_manager.execute(skill_name="GetUp")

        elif self.agent.world.playmode is PlayModeEnum.PLAY_ON:
            self.carry_ball()
        elif self.agent.world.playmode in (PlayModeEnum.BEFORE_KICK_OFF, PlayModeEnum.THEIR_GOAL, PlayModeEnum.OUR_GOAL):
            self.agent.skills_manager.execute("Neutral")
        else:
            self.carry_ball()

        self.agent.robot.commit_motor_targets_pd()

    def carry_ball(self):
        """
        Basic example of a behavior: moves the robot toward the goal while handling the ball.
        """
        their_goal_pos = self.agent.world.field.get_their_goal_position()[:2]
        ball_pos = self.agent.world.ball_pos[:2]
        my_pos = self.agent.world.global_position[:2]

        ball_to_goal = their_goal_pos - ball_pos
        bg_norm = np.linalg.norm(ball_to_goal)
        if bg_norm == 0:
            return
        ball_to_goal_dir = ball_to_goal / bg_norm

        dist_from_ball_to_start_carrying = 0.30
        carry_ball_pos = ball_pos - ball_to_goal_dir * dist_from_ball_to_start_carrying

        my_to_ball = ball_pos - my_pos
        my_to_ball_norm = np.linalg.norm(my_to_ball)
        if my_to_ball_norm == 0:
            my_to_ball_dir = np.zeros(2)
        else:
            my_to_ball_dir = my_to_ball / my_to_ball_norm

        cosang = np.dot(my_to_ball_dir, ball_to_goal_dir)
        cosang = np.clip(cosang, -1.0, 1.0)
        angle_diff = np.arccos(cosang)

        ANGLE_TOL = np.deg2rad(7.5)
        aligned = (my_to_ball_norm > 1e-6) and (angle_diff <= ANGLE_TOL)

        behind_ball = np.dot(my_pos - ball_pos, ball_to_goal_dir) < 0
        desired_orientation = MathOps.vector_angle(ball_to_goal)

        if not aligned or not behind_ball:
            self.agent.skills_manager.execute(
                "Walk",
                target_2d=carry_ball_pos,
                is_target_absolute=True,
                orientation=None if np.linalg.norm(my_pos - carry_ball_pos) > 2 else desired_orientation
            )
        else:
            self.agent.skills_manager.execute(
                "Walk",
                target_2d=their_goal_pos,
                is_target_absolute=True,
                orientation=desired_orientation
            )