File size: 2,922 Bytes
f0c8b65 | 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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | import tensorflow as tf
import os
from PPO_Model import PPOAgent
gpus = tf.config.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
print(e)
agent_config = {
"env_id": "CarRacing-v3",
"num_envs": 21,
"gamma": 0.99,
"lam": 0.95,
"clip_epsilon": 0.2,
"actor_lr": 3e-4,
"critic_lr": 3e-4,
"ppo_epochs": 10,
"minibatches": 4,
"steps_per_batch": 1024,
"num_stack_frames": 4,
"resize_dim": (84, 84),
"grayscale": True,
"seed": 42,
"log_dir": "./ppo_car_racing_logs",
"entropy_coeff": 0.01,
'save_interval_timesteps': 537600,
'hidden_layer_sizes': [512, 512, 512]
}
if __name__ == "__main__":
print("Initializing PPO Agent for evaluation...")
agent = PPOAgent(**agent_config)
root_log_dir = "./ppo_car_racing_logs"
latest_log_run_dir = None
if os.path.exists(root_log_dir):
all_runs = [os.path.join(root_log_dir, d) for d in os.listdir(root_log_dir) if os.path.isdir(os.path.join(root_log_dir, d))]
if all_runs:
latest_log_run_dir = max(all_runs, key=os.path.getmtime)
print(f"Found latest training run directory: {latest_log_run_dir}")
else:
print(f"No training run directories found in {root_log_dir}.")
else:
print(f"Log directory {root_log_dir} does not exist. Cannot find trained model.")
model_to_load = None
if latest_log_run_dir:
final_model_path = os.path.join(latest_log_run_dir, "final_model.weights.h5")
if os.path.exists(final_model_path):
model_to_load = final_model_path
else:
print(f"Final model weights not found in {latest_log_run_dir}. Checking checkpoints...")
checkpoint_dir = os.path.join(latest_log_run_dir, "checkpoints")
if os.path.exists(checkpoint_dir):
all_checkpoints = [os.path.join(checkpoint_dir, f) for f in os.listdir(checkpoint_dir) if f.endswith(".weights.h5")]
if all_checkpoints:
model_to_load = max(all_checkpoints, key=os.path.getmtime)
print(f"Loading latest checkpoint: {model_to_load}")
else:
print("No checkpoints found.")
else:
print("Checkpoints directory does not exist.")
if model_to_load:
print("\n--- Evaluation ---")
agent.evaluate(num_episodes=10, render=True, model_path=model_to_load)
else:
print("No trained model found to evaluate. Please train an agent first.") |