def __init__()

in train.py [0:0]

• 38 lines of code
• 3 McCabe index (conditional complexity)

    def __init__(self, cfg):
        self.work_dir = os.getcwd()
        print(f'workspace: {self.work_dir}')

        self.cfg = cfg

        self.logger = Logger(self.work_dir,
                             save_tb=cfg.log_save_tb,
                             log_frequency=cfg.log_freq,
                             agent='sac_svg')

        utils.set_seed_everywhere(cfg.seed)
        self.device = torch.device(cfg.device)
        self.env = utils.make_norm_env(cfg)
        self.episode = 0
        self.episode_step = 0
        self.episode_reward = 0
        self.done = False

        cfg.obs_dim = int(self.env.observation_space.shape[0])
        cfg.action_dim = self.env.action_space.shape[0]
        cfg.action_range = [
            float(self.env.action_space.low.min()),
            float(self.env.action_space.high.max())
        ]
        self.agent = hydra.utils.instantiate(cfg.agent)

        if isinstance(cfg.replay_buffer_capacity, str):
            cfg.replay_buffer_capacity = int(eval(cfg.replay_buffer_capacity))

        self.replay_buffer = ReplayBuffer(
            self.env.observation_space.shape,
            self.env.action_space.shape,
            int(cfg.replay_buffer_capacity),
            self.device,
            normalize_obs=cfg.normalize_obs,
        )
        self.replay_dir = os.path.join(self.work_dir, 'replay')

        self.video_recorder = VideoRecorder(
            self.work_dir if cfg.save_video else None)

        self.step = 0
        self.steps_since_eval = 0
        self.steps_since_save = 0
        self.best_eval_rew = None