默认的算法参数配置在joyrl/algos/[algo_name]/config.py中，具体请分别参考各算法说明。
Q-learning
class AlgoConfig:
def __init__(self) -> None:
self.epsilon_start = 0.95 # epsilon start value
self.epsilon_end = 0.01 # epsilon end value
self.epsilon_decay = 300 # epsilon decay rate
self.gamma = 0.90 # discount factor
self.lr = 0.1 # learning rate
注意：
设置epsilon_start=epsilon_end可以得到固定的epsilon=epsilon_end。
参数说明：
适当调整epsilon_decay以保证epsilon在训练过程中不会过早衰减。
- 由于传统强化学习算法面对的环境都比较简单，因此
gamma一般设置为0.9，且lr可以设置得比较大如0.1，不用太担心过拟合的情况。
DQN
class AlgoConfig(DefaultConfig):
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'}]