agents/version_2/watch_agent.py

from stable_baselines3 import DQN
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
import gymnasium as gym

import argparse

# This script should have some options
# 1. Turn off the stochasticity as determined by the ALEv5
#   Even if deterministic is set to true in evaluate policy, the environment will ignore this 25% of the time
#   To compensate for this, we can set the repeat action probability to 0

parser = argparse.ArgumentParser()
parser.add_argument("-r", "--repeat_action_probability", help="repeat action probability", type=float, default=0.25)
args = parser.parse_args()

MODEL_NAME = "ALE-Pacman-v5"
rpt_act_prob = args.repeat_action_probability

loaded_model = DQN.load(MODEL_NAME)

# Retrieve the environment
eval_env = Monitor(gym.make("ALE/Pacman-v5", render_mode="rgb_array", repeat_action_probability=rpt_act_prob))

# Evaluate the policy
mean_rwd, std_rwd = evaluate_policy(loaded_model.policy, eval_env, n_eval_episodes=1)
print("mean rwd: ", mean_rwd)
print("std rwd: ", std_rwd)