Reinforced-Learning-Godot/rl/Lib/site-packages/gymnasium/envs/mujoco/ant_v3.py

import numpy as np

from gymnasium import utils
from gymnasium.envs.mujoco import MuJocoPyEnv
from gymnasium.spaces import Box


DEFAULT_CAMERA_CONFIG = {
    "distance": 4.0,
}


class AntEnv(MuJocoPyEnv, utils.EzPickle):
    metadata = {
        "render_modes": [
            "human",
            "rgb_array",
            "depth_array",
        ],
        "render_fps": 20,
    }

    def __init__(
        self,
        xml_file="ant.xml",
        ctrl_cost_weight=0.5,
        contact_cost_weight=5e-4,
        healthy_reward=1.0,
        terminate_when_unhealthy=True,
        healthy_z_range=(0.2, 1.0),
        contact_force_range=(-1.0, 1.0),
        reset_noise_scale=0.1,
        exclude_current_positions_from_observation=True,
        **kwargs,
    ):
        utils.EzPickle.__init__(
            self,
            xml_file,
            ctrl_cost_weight,
            contact_cost_weight,
            healthy_reward,
            terminate_when_unhealthy,
            healthy_z_range,
            contact_force_range,
            reset_noise_scale,
            exclude_current_positions_from_observation,
            **kwargs,
        )

        self._ctrl_cost_weight = ctrl_cost_weight
        self._contact_cost_weight = contact_cost_weight

        self._healthy_reward = healthy_reward
        self._terminate_when_unhealthy = terminate_when_unhealthy
        self._healthy_z_range = healthy_z_range

        self._contact_force_range = contact_force_range

        self._reset_noise_scale = reset_noise_scale

        self._exclude_current_positions_from_observation = (
            exclude_current_positions_from_observation
        )

        if exclude_current_positions_from_observation:
            observation_space = Box(
                low=-np.inf, high=np.inf, shape=(111,), dtype=np.float64
            )
        else:
            observation_space = Box(
                low=-np.inf, high=np.inf, shape=(113,), dtype=np.float64
            )

        MuJocoPyEnv.__init__(
            self, xml_file, 5, observation_space=observation_space, **kwargs
        )

    @property
    def healthy_reward(self):
        return (
            float(self.is_healthy or self._terminate_when_unhealthy)
            * self._healthy_reward
        )

    def control_cost(self, action):
        control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
        return control_cost

    @property
    def contact_forces(self):
        raw_contact_forces = self.sim.data.cfrc_ext
        min_value, max_value = self._contact_force_range
        contact_forces = np.clip(raw_contact_forces, min_value, max_value)
        return contact_forces

    @property
    def contact_cost(self):
        contact_cost = self._contact_cost_weight * np.sum(
            np.square(self.contact_forces)
        )
        return contact_cost

    @property
    def is_healthy(self):
        state = self.state_vector()
        min_z, max_z = self._healthy_z_range
        is_healthy = np.isfinite(state).all() and min_z <= state[2] <= max_z
        return is_healthy

    @property
    def terminated(self):
        terminated = not self.is_healthy if self._terminate_when_unhealthy else False
        return terminated

    def step(self, action):
        xy_position_before = self.get_body_com("torso")[:2].copy()
        self.do_simulation(action, self.frame_skip)
        xy_position_after = self.get_body_com("torso")[:2].copy()

        xy_velocity = (xy_position_after - xy_position_before) / self.dt
        x_velocity, y_velocity = xy_velocity

        ctrl_cost = self.control_cost(action)
        contact_cost = self.contact_cost

        forward_reward = x_velocity
        healthy_reward = self.healthy_reward

        rewards = forward_reward + healthy_reward
        costs = ctrl_cost + contact_cost

        reward = rewards - costs
        terminated = self.terminated
        observation = self._get_obs()
        info = {
            "reward_forward": forward_reward,
            "reward_ctrl": -ctrl_cost,
            "reward_contact": -contact_cost,
            "reward_survive": healthy_reward,
            "x_position": xy_position_after[0],
            "y_position": xy_position_after[1],
            "distance_from_origin": np.linalg.norm(xy_position_after, ord=2),
            "x_velocity": x_velocity,
            "y_velocity": y_velocity,
            "forward_reward": forward_reward,
        }

        if self.render_mode == "human":
            self.render()
        return observation, reward, terminated, False, info

    def _get_obs(self):
        position = self.sim.data.qpos.flat.copy()
        velocity = self.sim.data.qvel.flat.copy()
        contact_force = self.contact_forces.flat.copy()

        if self._exclude_current_positions_from_observation:
            position = position[2:]

        observations = np.concatenate((position, velocity, contact_force))

        return observations

    def reset_model(self):
        noise_low = -self._reset_noise_scale
        noise_high = self._reset_noise_scale

        qpos = self.init_qpos + self.np_random.uniform(
            low=noise_low, high=noise_high, size=self.model.nq
        )
        qvel = (
            self.init_qvel
            + self._reset_noise_scale * self.np_random.standard_normal(self.model.nv)
        )
        self.set_state(qpos, qvel)

        observation = self._get_obs()

        return observation

    def viewer_setup(self):
        assert self.viewer is not None
        for key, value in DEFAULT_CAMERA_CONFIG.items():
            if isinstance(value, np.ndarray):
                getattr(self.viewer.cam, key)[:] = value
            else:
                setattr(self.viewer.cam, key, value)