run_evogym_diff.py

import sys
import os
import json
import torch

CURR_DIR = os.path.dirname(os.path.abspath(__file__))

LIB_DIR = os.path.join(CURR_DIR, "libs")
sys.path.append(LIB_DIR)
from experiment_utils import initialize_experiment, load_experiment


from gym_utils import load_robot
import custom_envs.parkour

from run_ppo_diff import run_ppo

from arguments.evogym_ppo import get_args


class ppoConfig:
    def __init__(self, args):
        self.num_processes = args.num_processes
        self.eval_processes = 1
        self.seed = 1

        self.steps = args.steps
        self.num_mini_batch = args.num_mini_batch
        self.epochs = args.epochs
        self.learning_rate = args.learning_rate
        self.gamma = args.gamma
        self.clip_range = args.clip_range
        self.ent_coef = 0.01
        self.vf_coef = 0.5
        self.max_grad_norm = 0.5
        self.lr_decay = True
        self.gae_lambda = 0.95
        self.init_log_std = args.init_log_std


def main():

    args = get_args()

    expt_path = os.path.join(CURR_DIR, "out", "evogym_poet", args.name)
    expt_args = load_experiment(expt_path)
    expt_niche = os.path.join(expt_path, "niche")
    niches = [
        name
        for name in os.listdir(expt_niche)
        if os.path.isdir(os.path.join(expt_niche, name))
    ]
    diff_niche = sorted(niches)

    dir_path = "./out_ppo/evogym_poet/default/niche"
    dire = [
        name
        for name in os.listdir(dir_path)
        if os.path.isdir(os.path.join(dir_path, name))
    ]
    directories = sorted(dire)
    if args.key + 5 > len(directories):
        end = len(directories)
    else:
        end = args.key + 5

    for k in range(args.key, end):
        model_path = os.path.join(dir_path, directories[k], "core", "best.pt")
        model = torch.load(model_path)
        for j in range(len(diff_niche)):
            niche_path = os.path.join(expt_path, "niche", diff_niche[j])
            assert os.path.exists(niche_path), f"no niche key {niche_path[j]}"
            result_path = os.path.join(niche_path, "diff_ppo_result_" + directories[k])
            initialize_experiment(args.name, result_path, args)

            robot = load_robot(CURR_DIR, expt_args["robot"])
            terrain_file = os.path.join(niche_path, "terrain.json")
            terrain = json.load(open(terrain_file, "r"))

            env_kwargs = dict(**robot, terrain=terrain)

            ppo_config = ppoConfig(args)

            for i in range(args.num):
                print(f"----------start ppo learning {i+1: 2}----------")
                save_path = os.path.join(result_path, str(i + 1))
                controller_path = os.path.join(save_path, "controller")
                os.makedirs(controller_path, exist_ok=True)

                history_file = os.path.join(save_path, "history.csv")
                run_ppo(
                    env_id=expt_args["task"],
                    robot=env_kwargs,
                    train_iters=args.train_iters,
                    eval_interval=args.evaluation_interval,
                    save_file=controller_path,
                    model=model,
                    config=ppo_config,
                    deterministic=True,
                    save_iter=True,
                    history_file=history_file,
                )
                print()


if __name__ == "__main__":
    main()