CartPole-v1_DQN_Test.py

# to test the trained model, you need to modify the "load_checkpoint=True" and the right "load_path" in the class GeneralConfig
import joyrl

class GeneralConfig():
    def __init__(self) -> None:
        self.env_name = "CartPole-v1" # name of environment
        self.algo_name = "DQN" # name of algorithm
        self.mode = "test" # train or test
        self.seed = 1 # random seed
        self.device = "cpu" # device to use
        self.train_eps = 100 # number of episodes for training
        self.test_eps = 20 # number of episodes for testing
        self.eval_eps = 10 # number of episodes for evaluation
        self.eval_per_episode = 5 # evaluation per episode
        self.max_steps = 200 # max steps for each episode
        self.load_checkpoint = True
        self.load_path = "Train_CartPole-v1_DQN_20221120-000359" # path to load model
        self.show_fig = False # show figure or not
        self.save_fig = True # save figure or not

class AlgoConfig():
    def __init__(self) -> None:
        # set epsilon_start=epsilon_end can obtain fixed epsilon=epsilon_end
        self.epsilon_start = 0.95  # epsilon start value
        self.epsilon_end = 0.01  # epsilon end value
        self.epsilon_decay = 500  # epsilon decay rate
        self.gamma = 0.95  # discount factor
        self.lr = 0.0001  # learning rate
        self.buffer_size = 100000  # size of replay buffer
        self.batch_size = 64  # batch size
        self.target_update = 4  # target network update frequency
        self.value_layers = [
            {'layer_type': 'linear', 'layer_dim': ['n_states', 256],
             'activation': 'relu'},
            {'layer_type': 'linear', 'layer_dim': [256, 256],
             'activation': 'relu'},
            {'layer_type': 'linear', 'layer_dim': [256, 'n_actions'],
             'activation': 'none'}]

if __name__ == "__main__":
    general_cfg = GeneralConfig()
    algo_cfg = AlgoConfig()
    joyrl.run(general_cfg,algo_cfg)