Yes, there can be problems when using a Softmax function to select actions in a Deep Q-Network (DQN).
The Softmax function is commonly used in reinforcement learning (RL) to select actions based on a set of Q-values for each action. However, there are a few potential issues with using Softmax in the context of DQNs:
Exploration-Exploitation Dilemma: The Softmax function always chooses an action with a nonzero probability, which means that there is always some level of exploration. However, the exploration rate can be too low, which can lead to the agent getting stuck in a suboptimal policy. Conversely, the exploration rate can be too high, which can lead to excessive random behavior that hinders learning.
Temperature Hyperparameter: The Softmax function has a temperature hyperparameter that determines the "stochasticity" of the action selection. If the temperature is too high, the agent will behave randomly; if the temperature is too low, the agent will behave deterministically. Finding the right temperature can be challenging, especially when the action space is large.
Large Action Space: If the action space is large, then the Softmax function can become computationally expensive, as it requires calculating the exponentials of all the Q-values.
To address these issues, several alternative action selection strategies have been proposed, such as the ε-greedy approach and the Boltzmann exploration approach. These approaches allow for a better balance between exploration and exploitation, and can be more efficient in terms of computation.
