softmaxQ

SoftmaxQ is a class of reinforcement learning algorithms that combines the ideas of softmax and Q-learning to learn policies in multi-agent settings. The softmaxQ algorithm was introduced as a solution to the exploration problem in multi-agent reinforcement learning, where agents need to learn policies that balance individual rewards with the need to cooperate and explore the environment.

In traditional Q-learning, the Q-function is used to estimate the expected discounted reward for each state-action pair. However, in multi-agent settings, the Q-function can become overly optimistic, leading to reinforcement learning loop instability. SoftmaxQ addresses this issue by using a softmax function to normalize the Q-values, which helps to avoid overestimation and promotes exploration.

The softmaxQ algorithm updates the Q-function using a policy-based approach, where agents learn by updating their policies rather than directly updating the Q-function. The policy is learned using a softmax function, which is applied to the Q-values to produce a distribution over actions. Each agent then chooses an action based on this distribution, and the loss function is designed to encourage exploration while learning policies that maximize cumulative rewards.

The softmaxQ algorithm has been applied to various multi-agent settings, including cooperative and competitive settings, and has shown improved performance over traditional Q-learning methods. However, the algorithm's convergence properties are still an area of ongoing research, and its application to more complex domains is an active area of investigation.