650 lines
24 KiB
Python
650 lines
24 KiB
Python
"""
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ml/env.py - Gymnasium Environment for JackBot Hexapod RL Training
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"""
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import time
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import math
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from enum import IntEnum
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from typing import Optional, Tuple, Dict, Any, List
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from collections import defaultdict
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import gymnasium as gym
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from gymnasium import spaces
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import numpy as np
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from stable_baselines3.common.callbacks import BaseCallback
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from config import cfg
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from Robot import Robot, PyBulletBackend
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from ml.SimManager import SimManager
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from ml.MetricsOverlay import MetricsHUD
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# Color Palette RGBA for Terminated/Failed Robots
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COLOR_FAILED = [0.3, 0.3, 0.3, 0.6]
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class CurriculumPhase(IntEnum):
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STAND_ONLY = 0
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FORWARD = 1
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TURN_AND_DIRECTION = 2
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OMNI_DIRECTION = 3
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FULL_COMMAND = 4
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class JackBotEnv(gym.Env):
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"""Gymnasium environment wrapping a single JackBot hexapod."""
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def __init__(
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self,
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use_gui: bool = True,
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random_command: bool = True,
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max_episode_steps: int = 3000,
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urdf_path: str = cfg.urdf_path,
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robot_mode: str = "direct",
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):
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super().__init__()
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self.robot_mode = robot_mode
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self.use_gui = use_gui
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self.random_command = random_command
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self.max_episode_steps = max_episode_steps
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self.urdf_path = urdf_path
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self.max_robot_speed = 0.6
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self.step_count = 0
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self.total_steps = 0
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self.cumulative_reward = 0.0
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self.robot_reward = 0.0
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self.consecutive_still_steps = 0
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self.is_failed = False
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self.episode_count = 0
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self.episode_height_sum = 0.0
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self.episode_roll_sum = 0.0
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self.episode_pitch_sum = 0.0
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self._curriculum_advanced = False
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self._first_reset = True
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# Reward Component Tracking Initialization
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self.last_reward_components: Dict[str, float] = {}
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self.episode_reward_components_sum: Dict[str, float] = defaultdict(float)
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# Dynamic Command Resampling Timing (60 Hz control loop)
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self.control_freq = 60
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self.min_cmd_hold_steps = int(2.0 * self.control_freq) # 120 steps (2s)
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self.max_cmd_hold_steps = int(6.0 * self.control_freq) # 360 steps (6s)
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self.next_cmd_resample_step = 0
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self.initial_stand_steps = 120 # Mandatory 2s standing window at episode reset
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# Initialize Simulation Manager
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self.sim_manager = SimManager(use_gui=self.use_gui)
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self.sim_manager.connect()
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# Connect physics world & load single robot
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self.plane, pb_robots, robot_joint_indices = self.sim_manager.load_scene(
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self.urdf_path, 0.0, self._robot_base_position
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)
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self.joint_indices = robot_joint_indices[0]
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# Instantiate Robot Python wrapper
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self.robot = Robot(
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backend_type=PyBulletBackend(self.sim_manager),
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urdf_path=self.urdf_path
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)
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action_dim = 18
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obs_dim = 18 + 4
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self.action_space = spaces.Box(-1.0, 1.0, shape=(action_dim,), dtype=np.float32)
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self.observation_space = spaces.Box(-np.inf, np.inf, shape=(obs_dim,), dtype=np.float32)
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self.command = np.zeros(4, dtype=np.float32)
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self.last_action = np.zeros(action_dim, dtype=np.float32)
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self.target_height = 0.122
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self.collapse_height_fraction = 0.55
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self.tilt_failure_rad = 0.9
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self.foot_link_indices = self._find_foot_link_indices()
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self.start_position = [0.0, 0.0, 0.0]
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self.max_distance_from_start = 0.0
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self.max_survival_steps = 0
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self.default_joint_angles = np.zeros(18, dtype=np.float32)
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# Curriculum Initialization via Enum
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self.curriculum_phase = CurriculumPhase.STAND_ONLY
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self.curriculum_stage_requirements = {
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CurriculumPhase.FORWARD: {
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"survival_steps": 300,
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"min_avg_height_ratio": 0.88,
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"max_avg_roll_pitch": 0.18,
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},
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CurriculumPhase.TURN_AND_DIRECTION: {
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"survival_steps": 500,
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"min_forward_distance": 2.5,
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"max_lateral_drift": 0.8,
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"min_avg_height_ratio": 0.85,
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"stability_roll_pitch": 0.25,
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},
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CurriculumPhase.OMNI_DIRECTION: {
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"survival_steps": 600,
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"min_distance": 5.0,
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"min_avg_height_ratio": 0.85,
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"stability_roll_pitch": 0.25,
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},
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CurriculumPhase.FULL_COMMAND: {
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"survival_steps": 750,
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"min_distance": 8.0,
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"min_avg_height_ratio": 0.85,
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"stability_roll_pitch": 0.20,
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},
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}
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self.hud = MetricsHUD(physics_client_id=self.sim_manager.physics_client)
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self.last_time = time.time()
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def _robot_base_position(self, robot_id: int, spacing: float = 0.0) -> list[float]:
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return [0.0, 0.0, 0.2]
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def _find_foot_link_indices(self) -> list:
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return self.sim_manager.get_foot_link_indices(self.pb_robot)
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def sample_command(self) -> np.ndarray:
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phase = self.curriculum_phase
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stand_probabilities = {
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CurriculumPhase.STAND_ONLY: 1.0,
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CurriculumPhase.FORWARD: 0.25,
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CurriculumPhase.TURN_AND_DIRECTION: 0.20,
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CurriculumPhase.OMNI_DIRECTION: 0.15,
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CurriculumPhase.FULL_COMMAND: 0.15,
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}
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if np.random.random() < stand_probabilities.get(phase, 0.15):
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return np.zeros(4, dtype=np.float32)
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if phase == CurriculumPhase.FORWARD:
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vx = np.random.uniform(0.15, 0.50)
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vy, vz, omega = 0.0, 0.0, 0.0
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elif phase == CurriculumPhase.TURN_AND_DIRECTION:
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vx = np.random.uniform(-0.8, 0.8)
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vy, vz = 0.0, 0.0
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omega = np.random.uniform(-0.8, 0.8)
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elif phase == CurriculumPhase.OMNI_DIRECTION:
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vx = np.random.uniform(-0.8, 0.8)
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vy = np.random.uniform(-0.5, 0.5)
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vz = 0.0
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omega = np.random.uniform(-0.8, 0.8)
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else:
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vx = np.random.uniform(-1.0, 1.0)
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vy = np.random.uniform(-1.0, 1.0)
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vz = 0.0
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omega = np.random.uniform(-1.0, 1.0)
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return np.array([vx, vy, vz, omega], dtype=np.float32)
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def reset(self, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None):
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super().reset(seed=seed)
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self.episode_count += 1
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self.step_count = 0
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self.cumulative_reward = 0.0
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self.robot_reward = 0.0
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self.is_failed = False
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self.episode_height_sum = 0.0
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self.episode_roll_sum = 0.0
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self.episode_pitch_sum = 0.0
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# Reset Component Tracking Dictionary
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self.last_reward_components = {}
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self.episode_reward_components_sum = defaultdict(float)
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spawn_pos = self._robot_base_position(0)
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spawn_orn = [0.0, 0.0, 0.0, 1.0]
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self.sim_manager.reset_robot_base(self.pb_robot, spawn_pos, spawn_orn)
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self.robot.reset_to_init()
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init_angles = self.robot.current_rad.data.flatten()
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self.sim_manager.hard_reset_joint_angles(init_angles, self.pb_robot)
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if self.use_gui:
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self.sim_manager.set_robot_color(self.pb_robot, [1.0, 1.0, 1.0, 1.0])
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self.consecutive_still_steps = 0
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self.last_action = np.zeros(self.action_space.shape[0], dtype=np.float32)
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self.max_distance_from_start = 0.0
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self.max_survival_steps = 0
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if self._first_reset:
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self.curriculum_phase = CurriculumPhase.STAND_ONLY
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self._first_reset = False
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self.command = np.zeros(4, dtype=np.float32)
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self.next_cmd_resample_step = self.initial_stand_steps
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pos, _ = self.sim_manager.get_robot_pose(self.pb_robot)
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self.start_position = [float(pos[0]), float(pos[1]), float(pos[2])]
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# Fixed single-robot settlement call
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self.target_height = self.sim_manager.settle_and_measure_height(
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steps=200, fallback_height=0.122
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)
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self.default_joint_angles = np.array(
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self.sim_manager.get_robot_joint_angles(self.pb_robot, self.joint_indices),
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dtype=np.float32
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)
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if self.use_gui:
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self.hud.reset()
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self._update_hud()
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return self._get_obs(), {}
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def get_reward_component_averages(self) -> Dict[str, float]:
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"""Calculates step-averaged scores for each sub-reward component."""
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steps = max(1, self.step_count)
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return {k: float(v / steps) for k, v in self.episode_reward_components_sum.items()}
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def get_current_robot_metrics(self) -> list:
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"""Returns metric summary for callbacks."""
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if self.is_failed:
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return []
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pos, _ = self.sim_manager.get_robot_pose(self.pb_robot)
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linear_vel, angular_vel = self.sim_manager.get_robot_velocity(self.pb_robot)
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start_x, start_y, _ = self.start_position
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dist = float(np.linalg.norm(np.array([pos[0] - start_x, pos[1] - start_y], dtype=np.float32)))
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speed = float(np.linalg.norm(np.array([linear_vel[0], linear_vel[1]], dtype=np.float32)))
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yaw_rate = float(abs(angular_vel[2]))
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return [{
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"reward": float(self.robot_reward),
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"distance_from_start": dist,
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"speed": speed,
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"yaw_rate": yaw_rate,
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"alive": True,
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"survival_steps": int(self.step_count),
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"phase_name": self.curriculum_phase.name,
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}]
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def _get_obs(self) -> np.ndarray:
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joint_angles = self.sim_manager.get_robot_joint_angles(self.pb_robot, self.joint_indices)
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return np.concatenate([joint_angles, self.command]).astype(np.float32)
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def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, bool, Dict[str, Any]]:
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self.step_count += 1
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self.total_steps += 1
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previous_action = self.last_action.copy()
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self.last_action = action.copy()
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if self.random_command and (self.step_count >= self.next_cmd_resample_step or self._curriculum_advanced):
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self.command = self.sample_command()
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random_interval = np.random.randint(self.min_cmd_hold_steps, self.max_cmd_hold_steps + 1)
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self.next_cmd_resample_step = self.step_count + random_interval
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self.robot.step_with_command(command=self.command, action=action, mode=self.robot_mode)
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if self.robot_mode != "kinematics_only" and self.step_count % 60 == 0:
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random_force = np.random.uniform(-2.0, 2.0, size=2)
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self.sim_manager.apply_external_force(
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body_id=self.pb_robot,
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force=[random_force[0], random_force[1], 0.0]
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)
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render_freq = 10
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if self.use_gui and self.step_count % render_freq != 0:
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self.sim_manager.set_rendering(False)
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self.sim_manager.step()
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if self.use_gui and self.step_count % render_freq == 0:
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self.sim_manager.set_rendering(True)
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self._update_robot_failure()
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self._update_distance_metrics()
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self._update_curriculum()
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obs = self._get_obs()
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reward = self._compute_reward(action, previous_action)
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self.cumulative_reward += reward
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self.robot_reward += reward
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terminated = self.is_failed
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truncated = self.step_count >= self.max_episode_steps
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info = {
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"reward_components": self.last_reward_components.copy()
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}
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if self.step_count % 120 == 0 and self.use_gui:
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self._update_hud()
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# Gymnasium standard 5-tuple return
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return obs, reward, terminated, truncated, info
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def _update_distance_metrics(self):
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pos, _ = self.sim_manager.get_robot_pose(self.pb_robot)
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self.episode_height_sum += float(pos[2])
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start_x, start_y, _ = self.start_position
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dist = float(np.linalg.norm(np.array([pos[0] - start_x, pos[1] - start_y], dtype=np.float32)))
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self.max_distance_from_start = max(self.max_distance_from_start, dist)
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self.max_survival_steps = max(self.max_survival_steps, self.step_count)
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def _phase_progress_ready(self, next_phase: CurriculumPhase) -> bool:
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if next_phase not in self.curriculum_stage_requirements:
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return False
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req = self.curriculum_stage_requirements[next_phase]
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survival_ok = self.max_survival_steps >= req["survival_steps"]
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avg_roll = self.episode_roll_sum / max(1, self.step_count)
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avg_pitch = self.episode_pitch_sum / max(1, self.step_count)
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max_allowed_angle = req.get("max_avg_roll_pitch", 0.20)
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stability_ok = (avg_roll <= max_allowed_angle) and (avg_pitch <= max_allowed_angle)
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avg_height = self.episode_height_sum / max(1, self.step_count)
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required_min_avg_height = self.target_height * req.get("min_avg_height_ratio", 0.85)
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height_ok = avg_height >= required_min_avg_height
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pos, _ = self.sim_manager.get_robot_pose(self.pb_robot)
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start_x, start_y, _ = self.start_position
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dx = pos[0] - start_x
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dy = pos[1] - start_y
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dist_2d = math.hypot(dx, dy)
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distance_ok = True
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if "min_forward_distance" in req:
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distance_ok = dx >= req["min_forward_distance"]
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elif "min_distance" in req:
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distance_ok = dist_2d >= req["min_distance"]
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drift_ok = True
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if "max_lateral_drift" in req:
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drift_ok = abs(dy) <= req["max_lateral_drift"]
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return survival_ok and height_ok and stability_ok and distance_ok and drift_ok
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def _update_curriculum(self):
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self._curriculum_advanced = False
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forced_forward = (self.curriculum_phase < CurriculumPhase.FORWARD) and (self.step_count >= 2500)
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if self.curriculum_phase < CurriculumPhase.FORWARD and (self._phase_progress_ready(CurriculumPhase.FORWARD) or forced_forward):
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self.curriculum_phase = CurriculumPhase.FORWARD
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self._curriculum_advanced = True
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reason = "FORCED (2500 steps)" if forced_forward else "MET"
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print(f"[Curriculum] Phase {self.curriculum_phase.name} unlocked [{reason}] at step {self.step_count}")
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elif self.curriculum_phase < CurriculumPhase.TURN_AND_DIRECTION and self._phase_progress_ready(CurriculumPhase.TURN_AND_DIRECTION):
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self.curriculum_phase = CurriculumPhase.TURN_AND_DIRECTION
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self._curriculum_advanced = True
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print(f"[Curriculum] Phase {self.curriculum_phase.name} unlocked at total step {self.total_steps}")
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elif self.curriculum_phase < CurriculumPhase.OMNI_DIRECTION and self._phase_progress_ready(CurriculumPhase.OMNI_DIRECTION):
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self.curriculum_phase = CurriculumPhase.OMNI_DIRECTION
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self._curriculum_advanced = True
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print(f"[Curriculum] Phase {self.curriculum_phase.name} unlocked at total step {self.total_steps}")
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elif self.curriculum_phase < CurriculumPhase.FULL_COMMAND and self._phase_progress_ready(CurriculumPhase.FULL_COMMAND):
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self.curriculum_phase = CurriculumPhase.FULL_COMMAND
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self._curriculum_advanced = True
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print(f"[Curriculum] Phase {self.curriculum_phase.name} unlocked at total step {self.total_steps}")
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def _compute_reward(self, action: np.ndarray, previous_action: np.ndarray) -> float:
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"""
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Calculates task rewards using normalized Exponential Kernels and tracks component terms.
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"""
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pos, (roll, pitch, yaw) = self.sim_manager.get_robot_pose_and_rpy(self.pb_robot)
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linear_vel, angular_vel = self.sim_manager.get_robot_velocity(self.pb_robot)
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current_joints = np.array(
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self.sim_manager.get_robot_joint_angles(self.pb_robot, self.joint_indices),
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dtype=np.float32
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)
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cmd_vx, cmd_vy, _, cmd_yaw = self.command
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cmd_norm = math.hypot(cmd_vx, cmd_vy)
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VEL_DEADBAND = 0.04
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YAW_DEADBAND = 0.05
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raw_speed = math.hypot(linear_vel[0], linear_vel[1])
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if raw_speed < VEL_DEADBAND:
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filtered_vx, filtered_vy = 0.0, 0.0
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filtered_speed = 0.0
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else:
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filtered_vx, filtered_vy = linear_vel[0], linear_vel[1]
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filtered_speed = raw_speed
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raw_yaw_rate = abs(angular_vel[2])
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if raw_yaw_rate < YAW_DEADBAND:
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filtered_yaw_rate = 0.0
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else:
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filtered_yaw_rate = angular_vel[2]
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height_error = pos[2] - self.target_height
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r_height = math.exp(-150.0 * (height_error ** 2))
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orientation_error = roll**2 + pitch**2
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r_stability = math.exp(-25.0 * orientation_error)
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joint_error = np.mean(np.square(current_joints - self.default_joint_angles))
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r_pose = math.exp(-2.0 * joint_error)
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action_delta = np.mean(np.square(action - previous_action))
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r_smoothness = math.exp(-0.1 * action_delta)
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r_lin_vel = 0.0
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r_ang_vel = 0.0
|
|
stillness_penalty = 0.0
|
|
gated_zero = 0.0
|
|
|
|
if cmd_norm < 0.05 and abs(cmd_yaw) < 0.05:
|
|
# STANDING MODE
|
|
w_height = 0.35
|
|
w_stability = 0.35
|
|
w_pose = 0.20
|
|
w_smoothness = 0.10
|
|
w_lin_vel = 0.0
|
|
w_ang_vel = 0.0
|
|
|
|
total_reward = (
|
|
(w_height * r_height)
|
|
+ (w_stability * r_stability)
|
|
+ (w_pose * r_pose)
|
|
+ (w_smoothness * r_smoothness)
|
|
)
|
|
else:
|
|
# WALKING / TURNING MODE
|
|
is_moving = (filtered_speed > 0.0) or (abs(filtered_yaw_rate) > 0.0)
|
|
|
|
target_vx = cmd_vx * self.max_robot_speed
|
|
target_vy = cmd_vy * self.max_robot_speed
|
|
target_speed = math.hypot(target_vx, target_vy)
|
|
|
|
if not is_moving:
|
|
gated_zero = 1.0
|
|
total_reward = 0.0
|
|
w_lin_vel, w_ang_vel, w_height, w_stability, w_pose, w_smoothness = 0, 0, 0, 0, 0, 0
|
|
else:
|
|
lin_vel_error = (filtered_vx - target_vx)**2 + (filtered_vy - target_vy)**2
|
|
r_lin_vel = math.exp(-25.0 * lin_vel_error)
|
|
|
|
ang_vel_error = (filtered_yaw_rate - cmd_yaw)**2
|
|
r_ang_vel = math.exp(-15.0 * ang_vel_error)
|
|
|
|
if target_speed > 0.08 and raw_speed < 0.03:
|
|
r_lin_vel = 0.0
|
|
stillness_penalty = -0.25
|
|
|
|
w_lin_vel = 0.55
|
|
w_ang_vel = 0.15
|
|
w_height = 0.10
|
|
w_stability = 0.12
|
|
w_pose = 0.0
|
|
w_smoothness = 0.08
|
|
|
|
total_reward = (
|
|
(w_lin_vel * r_lin_vel)
|
|
+ (w_ang_vel * r_ang_vel)
|
|
+ (w_height * r_height)
|
|
+ (w_stability * r_stability)
|
|
+ (w_smoothness * r_smoothness)
|
|
+ stillness_penalty
|
|
)
|
|
|
|
final_reward = float(total_reward / 10.0)
|
|
|
|
comp = {
|
|
"lin_vel": float((w_lin_vel * r_lin_vel) / 10.0),
|
|
"ang_vel": float((w_ang_vel * r_ang_vel) / 10.0),
|
|
"height": float((w_height * r_height) / 10.0),
|
|
"stability": float((w_stability * r_stability) / 10.0),
|
|
"pose": float((w_pose * r_pose) / 10.0),
|
|
"smoothness": float((w_smoothness * r_smoothness) / 10.0),
|
|
"stillness_penalty": float(stillness_penalty / 10.0),
|
|
"gated_zero": gated_zero,
|
|
"total_step_reward": final_reward,
|
|
}
|
|
|
|
self.last_reward_components = comp
|
|
for key, val in comp.items():
|
|
self.episode_reward_components_sum[key] += val
|
|
|
|
return final_reward
|
|
|
|
def _update_hud(self):
|
|
if not self.use_gui:
|
|
return
|
|
|
|
now = time.time()
|
|
fps = 1.0 / max(now - self.last_time, 1e-5)
|
|
self.last_time = now
|
|
|
|
pos, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(self.pb_robot)
|
|
|
|
self.hud.update(
|
|
episode=self.episode_count,
|
|
step=self.total_steps,
|
|
robot_rewards=[self.robot_reward],
|
|
cmd_vel=self.command,
|
|
fps=fps,
|
|
height=pos[2],
|
|
roll_pitch=(math.degrees(roll), math.degrees(pitch))
|
|
)
|
|
|
|
def _update_robot_failure(self):
|
|
if self.is_failed:
|
|
return
|
|
|
|
if self.step_count < 15:
|
|
return
|
|
|
|
position, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(self.pb_robot)
|
|
collapse_threshold = max(0.04, self.collapse_height_fraction * self.target_height)
|
|
is_tilted = abs(roll) > self.tilt_failure_rad or abs(pitch) > self.tilt_failure_rad
|
|
is_collapsed = position[2] < collapse_threshold
|
|
|
|
if is_tilted or is_collapsed:
|
|
self.is_failed = True
|
|
if self.use_gui:
|
|
self.sim_manager.set_robot_color(self.pb_robot, COLOR_FAILED)
|
|
|
|
def close(self):
|
|
self.sim_manager.disconnect()
|
|
|
|
|
|
class CurriculumCallback(BaseCallback):
|
|
"""Logs curriculum phase breakdown and best performance metrics to TensorBoard."""
|
|
|
|
def __init__(self, verbose=0):
|
|
super().__init__(verbose)
|
|
self.best_speed = 0.0
|
|
self.best_yaw_rate = 0.0
|
|
self.best_distance = 0.0
|
|
self.best_survival_steps = 0.0
|
|
self.best_reward = -float('inf')
|
|
|
|
def _on_step(self) -> bool:
|
|
return True
|
|
|
|
def _on_rollout_end(self) -> bool:
|
|
try:
|
|
vec_env = self.training_env
|
|
alive_metrics = vec_env.env_method("get_current_robot_metrics")
|
|
|
|
self.best_speed = 0.0
|
|
self.best_yaw_rate = 0.0
|
|
self.best_distance = 0.0
|
|
self.best_survival_steps = 0.0
|
|
self.best_reward = -float('inf')
|
|
|
|
phase_counts = {
|
|
"stand_only": 0,
|
|
"forward": 0,
|
|
"turn_and_direction": 0,
|
|
"omni_direction": 0,
|
|
"full_command": 0,
|
|
}
|
|
|
|
for worker_res in alive_metrics:
|
|
for metrics in worker_res:
|
|
if not metrics.get("alive", False):
|
|
continue
|
|
|
|
phase_key = metrics.get("phase_name", "STAND_ONLY").lower()
|
|
if phase_key in phase_counts:
|
|
phase_counts[phase_key] += 1
|
|
|
|
if metrics["reward"] > self.best_reward:
|
|
self.best_reward = float(metrics["reward"])
|
|
if metrics["speed"] > self.best_speed:
|
|
self.best_speed = float(metrics["speed"])
|
|
if metrics["yaw_rate"] > self.best_yaw_rate:
|
|
self.best_yaw_rate = float(metrics["yaw_rate"])
|
|
if metrics["distance_from_start"] > self.best_distance:
|
|
self.best_distance = float(metrics["distance_from_start"])
|
|
if metrics["survival_steps"] > self.best_survival_steps:
|
|
self.best_survival_steps = float(metrics["survival_steps"])
|
|
|
|
for phase_name, count in phase_counts.items():
|
|
self.logger.record(f"phase/{phase_name}", count)
|
|
|
|
self.logger.record("custom/best_reward", float(self.best_reward) if np.isfinite(self.best_reward) else 0.0)
|
|
self.logger.record("custom/best_survival_steps", float(self.best_survival_steps))
|
|
self.logger.record("custom/best_distance_from_start_m", float(self.best_distance))
|
|
self.logger.record("custom/best_speed_mps", float(self.best_speed))
|
|
self.logger.record("custom/best_yaw_rate_rads", float(self.best_yaw_rate))
|
|
|
|
except Exception:
|
|
pass
|
|
|
|
return True
|
|
|
|
|
|
class RewardLoggerCallback(BaseCallback):
|
|
"""
|
|
Logs step-averaged individual reward components to TensorBoard during PPO training.
|
|
"""
|
|
|
|
def __init__(self, verbose=0):
|
|
super().__init__(verbose)
|
|
|
|
def _on_step(self) -> bool:
|
|
return True
|
|
|
|
def _on_rollout_end(self) -> bool:
|
|
try:
|
|
vec_env = self.training_env
|
|
all_comp_averages = vec_env.env_method("get_reward_component_averages")
|
|
|
|
if not all_comp_averages:
|
|
return True
|
|
|
|
keys = all_comp_averages[0].keys()
|
|
for key in keys:
|
|
avg_val = np.mean([env_comp.get(key, 0.0) for env_comp in all_comp_averages])
|
|
self.logger.record(f"reward_components/{key}", float(avg_val))
|
|
|
|
except Exception:
|
|
pass
|
|
|
|
return True |