如何设计处理非量化反馈的强化学习（Reinforcement Learning）算法？

Hey there! Let's break down how to solve this problem—since you're dealing with non-numeric feedback instead of the usual quantitative labels we see in most ML tutorials, we need to reframe the learning task a bit. Here are some practical approaches you can try, whether you stick with reinforcement learning or explore other methods:

1. Map Feedback to Reward Signals (Reinforcement Learning Route) #

Reinforcement learning is built around reward signals, so this is a natural fit. You can assign a numerical reward to each feedback type based on how positive it is—you don't need perfect values, just a clear relative order. For example:

• exact: +10 (max reward for getting it right)
• very close: +7
• close: +3
• far: -2
• too far: -5

Then use a basic RL algorithm like Q-learning or Policy Gradient. You could use a small neural network as your Q-function or policy network: feed in your n input features, and have it output either Q-values for possible number predictions or the predicted number directly. After each prediction, calculate the reward from your feedback and update the network's parameters to maximize future rewards.

2. Treat It as an Ordinal Classification Task (Supervised Learning Route) #

Your feedback has a clear order: too far < far < close < very close < exact. This is an ordinal classification problem, not a standard multi-class task where all categories are independent.

Here's how to approach it:

• Assign a rank label to each feedback type: e.g., 0 = too far, 1 = far, 2 = close, 3 = very close, 4 = exact
• Instead of cross-entropy loss (used for standard multi-class), use an ordinal regression loss (like cumulative link loss) or even MSE loss (treating the rank as a continuous value works surprisingly well here because of the clear order)

Your model can output a number, and you'll train it to align with the rank of your feedback. Over time, it'll learn to adjust predictions to move toward higher-ranked feedback.

3. Interactive Learning with Bayesian Optimization #

Since you're providing feedback in real-time, Bayesian optimization is a great fit. It uses a Gaussian Process to model the relationship between your input features, predicted numbers, and your feedback. Each time you give feedback, it updates its model to figure out which number to predict next to get the most useful information—gradually narrowing in on your target.

This method doesn't require tons of data upfront and works well for interactive, iterative tasks like this.

4. Simple Iterative Rule-Based Adjustment (Quick Prototype) #

If you want to start small without diving into complex models, try a straightforward iterative approach:

• Start with an initial prediction (either random or a simple linear combination of your inputs)
• Adjust the prediction based on feedback:
  • too far: Make a big adjustment (e.g., add/subtract a large step, or multiply/divide by 1.5)
  • far: Make a moderate adjustment
  • close/very close: Make a small tweak
  • exact: Stop or lock in that prediction
• Over time, you can refine the adjustment rules based on which inputs lead to better predictions, then convert those rules into a more formal model later.

Quick Tips #

• Keep track of every input, prediction, and feedback pair. As you accumulate data, you can use it to train a supervised learning model later.
• Don't stress about getting reward values or rank labels perfect—models care more about the relative difference between feedback types than the exact numbers.
• Start with simple models (like linear regression or a tiny neural network) to test your approach before moving to more complex ones.

内容的提问来源于stack exchange，提问作者ffff