print("-----------") if save_gif: if verbose: print("Saving the gif of the episodes") save_log_gif(logs_path, res) return agent, res if __name__ == "__main__": from grid2op.Reward import L2RPNSandBoxScore, L2RPNReward from l2rpn_baselines.utils import cli_eval # Parse command line args = cli_eval().parse_args() # Create dataset env env = make(args.env_name, reward_class=L2RPNSandBoxScore, other_rewards={"reward": L2RPNReward}) # Call evaluation interface evaluate(env, name=args.name, load_path=os.path.abspath(args.load_path), logs_path=args.logs_dir, nb_episode=args.nb_episode, nb_process=args.nb_process, max_steps=args.max_steps, verbose=args.verbose,
nb_process=nb_process, max_iter=max_steps, pbar=False) # Print summary print("Evaluation summary:") for _, chron_name, cum_reward, nb_time_step, max_ts in res: msg_tmp = "\tFor chronics located at {}\n".format(chron_name) msg_tmp += "\t\t - cumulative reward: {:.6f}\n".format(cum_reward) msg_tmp += "\t\t - number of time steps completed: {:.0f} / {:.0f}".format( nb_time_step, max_ts) print(msg_tmp) if save_gif: save_log_gif(load_path, res) if __name__ == "__main__": import grid2op from l2rpn_baselines.utils import cli_eval args_cli = cli_eval().parse_args() env = grid2op.make() evaluate(env, load_path=args_cli.load_path, logs_path=args_cli.logs_path, nb_episode=args_cli.nb_episode, nb_process=args_cli.nb_process, max_steps=args_cli.max_steps, verbose=args_cli.verbose, save_gif=args_cli.save_gif)