File size: 2,359 Bytes
ebb75df c37ff18 ebb75df cbe0575 c37ff18 cbe0575 a30d4ce 1427095 a30d4ce 1427095 c37ff18 a30d4ce cbe0575 a30d4ce c37ff18 ebb75df a30d4ce cbe0575 ebb75df cbe0575 ebb75df cbe0575 ebb75df a30d4ce c37ff18 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
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
# DONE
# 2. Print out the evaluation metrics or save to file
# 3. Render in the ALE or not
# DONE
# 4. Print the keyword args for the environment? I think this might be helpful...
# IN PROGRESS
# 5. Add option flag to accept file path for model
# DONE
# 6. Add option flag to accept number of episodes
# DONE
parser = argparse.ArgumentParser()
parser.add_argument("-r", "--repeat_action_probability", help="repeat action probability, default 0.25", type=float, default=0.25)
parser.add_argument("-f", "--frameskip", help="frameskip, default 4", type=int, default=4)
parser.add_argument("-o", "--observe", help="observe agent", action="store_const", const=True)
parser.add_argument("-p", "--print", help="print environment information", action="store_const", const=True)
parser.add_argument("-e", "--num_episodes", help="specify the number of episodes to evaluate, default 1", type=int, default=1)
parser.add_argument("-a", "--agent_filepath", help="file path to agent to watch, minus the .zip extension", type=str, required=True)
args = parser.parse_args()
MODEL_NAME = args.agent_filepath
loaded_model = DQN.load(MODEL_NAME)
# Toggle the render mode based on the -o flag
if args.observe == True:
mode = "human"
else:
mode = "rgb_array"
# Retrieve the environment
eval_env = Monitor(gym.make("ALE/Pacman-v5",
render_mode=mode,
repeat_action_probability=args.repeat_action_probability,
frameskip=args.frameskip,))
if args.print == True:
env_info = str(eval_env.spec).split(", ")
for item in env_info:
print(item)
# Evaluate the policy
mean_rwd, std_rwd = evaluate_policy(loaded_model.policy, eval_env, n_eval_episodes=args.num_episodes)
print("eval episodes: ", args.num_episodes)
print("mean rwd: ", mean_rwd)
print("std rwd: ", std_rwd) |