#!/bin/bash


# baseline
python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/reward_norm/medium_mu_chi_b_1_guidance/ppo/baseline/128_128_2e8steps_seed_1/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 448899 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/baseline/seed_1.csv
python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/reward_norm/medium_mu_chi_b_1_guidance/ppo/baseline/128_128_2e8steps_seed_2/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 528493 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/baseline/seed_2.csv
python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/reward_norm/medium_mu_chi_b_1_guidance/ppo/baseline/128_128_2e8steps_seed_3/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 217198 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/baseline/seed_3.csv
python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/reward_norm/medium_mu_chi_b_1_guidance/ppo/baseline/128_128_2e8steps_seed_4/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 427989 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/baseline/seed_4.csv
python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/reward_norm/medium_mu_chi_b_1_guidance/ppo/baseline/128_128_2e8steps_seed_5/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 247904 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/baseline/seed_5.csv

# rl_bc iter_1
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_1/128_128_2e8steps_lambda_1e-3_1/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 48793 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_1_seed_1.csv
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_1/128_128_2e8steps_lambda_1e-3_2/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 43332 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_1_seed_2.csv
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_1/128_128_2e8steps_lambda_1e-3_3/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 76722 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_1_seed_3.csv
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_1/128_128_2e8steps_lambda_1e-3_4/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 42357 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_1_seed_4.csv
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_1/128_128_2e8steps_lambda_1e-3_5/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 68933 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_1_seed_5.csv

# rl_bc iter_4
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_4/256_256_128_128_64_5e8steps_lambda_1e-1_1/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 3357 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_4_seed_1.csv
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_4/256_256_128_128_64_5e8steps_lambda_1e-1_2/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 4894 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_4_seed_2.csv
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_4/256_256_128_128_64_5e8steps_lambda_1e-1_3/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 5599 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_4_seed_3.csv
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_4/256_256_128_128_64_5e8steps_lambda_1e-1_4/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 7046 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_4_seed_4.csv
# python train_scripts/reward_norm/evaluations/evaluation.py --algo ppo --algo-ckpt checkpoints/IRPO/rl/guidance_law_mode/iter_4/256_256_128_128_64_5e8steps_lambda_1e-1_5/best_model.zip --env-config configs/env/reward_norm/env_config_guidance_MR_medium_mu_chi_b_1.json --n-envs 8 --eval-episode-num 1000 --seed 6210 --save-result --result-file-save-name train_scripts/reward_norm/evaluations/results/medium_mu_chi_b_1_guidance/ppo/rl_bc/iter_4_seed_5.csv
