Created
March 4, 2022 10:16
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def objective( | |
trial: optuna.trial.Trial, | |
force_linear_model: bool = False, | |
n_episodes_to_train: int = 200, | |
) -> float: | |
""" | |
Samples hyperparameters, trains, and evaluates the RL agent. | |
It outputs the average reward on 1,000 episodes. | |
""" | |
env_name = 'CartPole-v1' | |
env = gym.make('CartPole-v1') | |
with mlflow.start_run(): | |
# generate unique agent_id | |
agent_id = get_agent_id(env_name) | |
mlflow.log_param('agent_id', agent_id) | |
# hyper-parameters | |
args = sample_hyper_parameters(trial, | |
force_linear_model=force_linear_model) | |
mlflow.log_params(trial.params) | |
# fix seeds to ensure reproducible runs | |
set_seed(env, args['seed']) | |
# create agent object | |
agent = QAgent( | |
env, | |
learning_rate=args['learning_rate'], | |
discount_factor=args['discount_factor'], | |
batch_size=args['batch_size'], | |
memory_size=args['memory_size'], | |
freq_steps_train=args['freq_steps_train'], | |
freq_steps_update_target=args['freq_steps_update_target'], | |
n_steps_warm_up_memory=args['n_steps_warm_up_memory'], | |
n_gradient_steps=args['n_gradient_steps'], | |
nn_hidden_layers=args['nn_hidden_layers'], | |
max_grad_norm=args['max_grad_norm'], | |
normalize_state=args['normalize_state'], | |
epsilon_start=args['epsilon_start'], | |
epsilon_end=args['epsilon_end'], | |
steps_epsilon_decay=args['steps_epsilon_decay'], | |
log_dir=TENSORBOARD_LOG_DIR / env_name / agent_id | |
) | |
# train loop | |
train(agent, | |
env, | |
n_episodes=n_episodes_to_train, | |
log_dir=TENSORBOARD_LOG_DIR / env_name / agent_id) | |
agent.save_to_disk(SAVED_AGENTS_DIR / env_name / agent_id) | |
# evaluate its performance | |
rewards, steps = evaluate(agent, env, n_episodes=1000, epsilon=0.00) | |
mean_reward = np.mean(rewards) | |
std_reward = np.std(rewards) | |
mlflow.log_metric('mean_reward', mean_reward) | |
mlflow.log_metric('std_reward', std_reward) | |
return mean_reward |
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