采用Reinforcement Learning、Q-Learning优化巫师施法顺序的可行性咨询

Great question—let’s unpack whether this Q-Learning approach makes sense, and if there are better alternatives for your scenario.

First: The Core Idea Is Reasonable #

Your problem fits neatly into the Markov Decision Process (MDP) framework, which is exactly what Q-Learning was built to solve:

• You’re making sequential decisions (which spell to cast each turn)
• Each action (spell) shifts the system’s state
• You have a clear, goal-oriented reward signal (kill all orcs as fast as possible, or avoid wizard death)

Q-Learning is a valid pick here, especially if:

• You don’t have a perfect, pre-defined environment model (e.g., some spells have probabilistic effects, or you’re unsure exactly how much damage each spell deals to orcs)
• You want the wizard to adapt to changing conditions (like if orc behavior or spell effects shift over time) through trial-and-error learning

But There Are Critical Details to Fix First #

Your initial state definition is incomplete, which would break the Markov property (a key requirement for Q-Learning to work):

Current state definition: "剩余法术集合" (remaining spells)
Missing key state variables: Orc status (number of surviving orcs, their remaining health, any active control effects like stuns).

Without including orc status in your state, the algorithm can’t tell the difference between facing 10 full-health orcs vs. 1 near-death orc—even if the remaining spell set is identical. This would lead to suboptimal or outright nonsensical decisions.

When Q-Learning Might Not Be the Best Choice #

Q-Learning is powerful, but it’s not always the most efficient option. Consider these alternatives based on your scenario:

• If you have a perfect environment model (e.g., you know exact spell damage, orc health values, and all effects are deterministic):
  Dynamic Programming (DP) or heuristic search algorithms like A* would be faster and more reliable. These methods can directly compute the optimal spell sequence without needing to "learn" through trial-and-error.
• If state space gets too big:
  Your initial state space (2^20 possible spell subsets) is already over 1 million, and adding orc status will make it even larger. Table-based Q-Learning will struggle with this "curse of dimensionality." Here, you’d need to switch to a function approximation-based Q-Learning variant (like DQN, Deep Q-Network) to represent the Q-value function with a neural network, compressing the state space.

Final Takeaway #

Your core idea is not wrong—Q-Learning can absolutely be used to solve this problem. But you’ll need to:

1. Expand your state definition to include orc status
2. Design a clear reward function (e.g., -1 reward per turn to encourage speed, +100 reward for killing all orcs, -100 reward for wizard death)
3. Choose between table-based Q-Learning, DQN, or non-RL methods based on whether you have a perfect environment model and the size of your state space.

内容的提问来源于stack exchange，提问作者Greg C