Q-Learning Values Going Through the Roof: How to Fix Overflow Issues in Your Golang Implementation?

Q-Learning Values Soaring: Identifying and Resolving the Issue

In an attempt to implement the Q-Learning algorithm using Golang, a recent implementation has encountered an overflow issue, with values reaching astronomical proportions. This article delves into the root cause of this problem and provides a practical solution to correct the escalating values.

Oversized Values in Reinforcement Learning

A key concern in Reinforcement Learning is that state-action values can grow excessively large. This phenomenon is a result of the optimization objective, where the agent aims to maximize the expected total reward. In this particular scenario, the algorithm assigns a positive reward at each time step, prompting the agent to extend the game indefinitely. Consequently, the Q-values escalate, as the agent continues to accrue rewards.

Redefining the Reward Function

The fundamental flaw in the implementation stems from an improperly defined reward function. To guide the agent towards a successful strategy, the reward should incentivize winning. However, the current reward function awards a positive value for every time step, effectively rewarding the agent for prolonging the game endlessly. This conflicting objective is what leads to the unrestrained growth of the Q-values.

Implementing a Negative Time Step Penalty

To resolve this issue, the reward function needs to be modified to include a negative penalty for each time step. This penalty effectively encourages the agent to seek an expeditious path to victory rather than dragging out the game needlessly. By enforcing a time limit, the reward function aligns with the desired outcome.

Additional Considerations

Alongside modifying the reward function, it's worth reviewing a few additional aspects of your code:

• Ensure that prevScore contains the previous step's reward and not the Q-value. This is because the Q-value is based on the reward and other factors.
• Consider using a data type that can accommodate larger values, such as float128, if necessary. While float64 has a limited range, float128 offers increased precision and can handle larger values.

By addressing these issues and incorporating the appropriate modifications, you should expect to witness a significant improvement in the behavior of your Q-Learning agent. The values should stabilize within an acceptable range, allowing the agent to learn optimal strategies.

The above is the detailed content of Q-Learning Values Going Through the Roof: How to Fix Overflow Issues in Your Golang Implementation?. For more information, please follow other related articles on the PHP Chinese website!