# 创建conda环境conda create -n asap python=3.8conda activate asap
# 安装基础依赖pip install hydra-core>=1.2.0pip install numpy==1.23.5pip install rich ipdb matplotlib termcolor wandb plotly tqdmpip install loguru meshcat pynput scipy tensorboard onnx onnxruntimepip install opencv-python joblib easydict lxml numpy-stl open3d

# 安装IsaacGym依赖cd isaacgym/pythonpip install -e .

# 安装HumanoidVerse包cd humanoidversepip install -e .

# 设置IsaacSim环境export ISAACSIM_PATH=/path/to/isaacsimcd isaaclabpip install -e .

import hydrafrom hydra.utils import instantiatefrom omegaconf import OmegaConf
@hydra.main(config_path="config", config_name="base")def main(config):    # 初始化仿真器    simulator_type = config.simulator['_target_'].split('.')[-1]        if simulator_type == 'IsaacSim':        from omni.isaac.lab.app import AppLauncher        app_launcher = AppLauncher(args_cli)        simulation_app = app_launcher.app        # 初始化环境和算法    env = instantiate(config=config.env, device=device)    algo = instantiate(device=device, env=env, config=config.algo)        # 开始训练    algo.learn()

from humanoidverse.envs.motion_tracking.motion_tracking import LeggedRobotMotionTrackingfrom humanoidverse.agents.ppo.ppo import PPO
# 初始化运动跟踪环境config = OmegaConf.load("config/motion_tracking.yaml")env = LeggedRobotMotionTracking(config, device="cuda:0")
# 初始化PPO算法algo = PPO(env, config, log_dir="./logs")algo.setup()algo.learn()

from humanoidverse.agents.callbacks.analysis_plot import AnalysisPlotMotionTracking
# 添加评估回调eval_callback = AnalysisPlotMotionTracking(config, training_loop=algo)algo.eval_callbacks.append(eval_callback)
# 执行策略评估algo.evaluate_policy()

import torchimport torch.nn as nnimport torch.optim as optim
class PPO(BaseAlgo):    def __init__(self, env, config, log_dir=None, device='cpu'):        self.device = device        self.env = env        self.config = config        self.log_dir = log_dir                # 初始化配置        self._init_config()                # 初始化模型和优化器        self._setup_models_and_optimizer()                # 初始化经验回放缓冲区        self._setup_storage()        def _setup_models_and_optimizer(self):        """设置Actor和Critic网络"""        self.actor = PPOActor(            obs_dim_dict=self.algo_obs_dim_dict,            module_config_dict=self.config.module_dict.actor,            num_actions=self.num_act,            init_noise_std=self.config.init_noise_std        ).to(self.device)                self.critic = PPOCritic(            self.algo_obs_dim_dict,            self.config.module_dict.critic        ).to(self.device)                # 初始化优化器        self.actor_optimizer = optim.Adam(            self.actor.parameters(),             lr=self.actor_learning_rate        )        self.critic_optimizer = optim.Adam(            self.critic.parameters(),             lr=self.critic_learning_rate        )

class LeggedRobotMotionTracking(LeggedRobotBase):    def __init__(self, config, device):        super().__init__(config, device)                # 初始化运动库        self._init_motion_lib()                # 初始化跟踪配置        self._init_tracking_config()                # 初始化保存设置        self._init_save_motion()        def _init_motion_lib(self):        """初始化运动数据库"""        from humanoidverse.utils.motion_lib.motion_lib_robot import MotionLibRobot                self.motion_lib = MotionLibRobot(            self.config.robot.motion,            device=self.device        )                # 加载预计算的运动数据        self.motion_data = self.motion_lib.load_motion_data(            self.config.motion_file        )

class DeltaDynamicsModel(BaseAlgo):    def __init__(self, env, config, log_dir=None, device='cpu'):        super().__init__(env, config, log_dir, device)                # 初始化神经网络模型        self.delta_model = DeltaDynamics_NN(            input_dim=self.env.config.robot.policy_obs_dim,            output_dim=self.env.config.robot.actions_dim        ).to(self.device)                # 初始化优化器        self.optimizer = optim.Adam(            self.delta_model.parameters(),            lr=self.config.learning_rate        )        def learn(self):        """训练Delta动力学模型"""        for iteration in range(self.config.num_learning_iterations):            # 收集数据            rollout_data = self._collect_rollout()                        # 计算损失            loss = self._compute_loss(rollout_data)                        # 反向传播            self.optimizer.zero_grad()            loss.backward()            self.optimizer.step()

class AnalysisPlotMotionTracking(RL_EvalCallback):    def __init__(self, config, training_loop):        super().__init__(config, training_loop)                # 初始化Web日志器        self.logger = WebLogger(self.config.sim_dt)                # 初始化缓冲区        self.reset_buffers()                # 设置机器人限制        self.logger.set_robot_limits(            self.env.dof_pos_limits.cpu().numpy(),            self.env.dof_vel_limits.cpu().numpy(),            self.env.torque_limits.cpu().numpy()        )        def on_pre_eval_env_step(self, actor_state):        """在环境步进前记录数据"""        # 记录观测值        self.obs_buf[env_id].append(actor_state['obs'])        self.act_buf[env_id].append(actor_state['action'])                return actor_state