Q-learning中固定ε与ε衰减的优劣对比及选型建议咨询

Hey there! Let's break this down for your maze-and-apple Q-learning agent—this is such a classic (and fun) reinforcement learning problem, so I’m glad you’re digging into the details of exploration strategies.

• Pros: Super simple to implement, and keeps consistent exploration throughout training. This can be useful if your environment had random, changing elements (like apples respawning in new spots), but for your static maze, the main upside is ease of debugging early on.
• Cons: The big downside here is wasted effort in later training phases. Once your agent has already mapped most of the maze and found apples, it’ll still randomly choose suboptimal actions (like backtracking to a dead end) just because ε is fixed. This slows down convergence to a stable, optimal strategy—you’ll spend extra episodes watching your agent fumble around when it already knows better.

• Pros: This is usually the better fit for static environments like your fixed maze with set apple positions. By starting with a high ε (e.g., 0.9) early on, you force the agent to explore every nook and cranny, ensuring it doesn’t miss any hidden apples or critical paths out of the maze. As training progresses, you gradually lower ε (either linearly, exponentially, or via a step function) to shift focus to exploitation—using the best paths it’s already learned to collect all apples and exit efficiently. This leads to faster convergence and a more reliable final strategy.
• Cons: The catch is getting the decay rate right. If you decay ε too fast, your agent might stop exploring before it’s found all apples or mapped the entire maze—leaving you with a suboptimal strategy that exits the maze but misses some fruit. If you decay it too slow, you’re back to the same problem as fixed ε: wasting time on unnecessary exploration late in training.

Since your environment is static (maze layout and apple positions don’t change), decaying ε-greedy is the way to go. Here’s how to tweak it for your use case:

• Start with an initial ε of 0.8–1.0: This ensures the agent prioritizes exploration first, so it doesn’t get stuck in a small section of the maze early on.
• Choose a decay schedule: Linear decay (e.g., subtract 0.001 from ε every episode until it hits 0.05) is easy to implement, or exponential decay (e.g., multiply ε by 0.99 every episode) works well for slower, smoother reduction.
• Keep a small minimum ε (0.01–0.05): Even in late training, a tiny bit of exploration prevents the agent from getting stuck in a local optimal (like a path that exits quickly but misses one apple—occasional random actions might help it find that last fruit).

If you’re still unsure, do a quick side-by-side test: Run a few hundred episodes with fixed ε=0.1, then another run with decaying ε, and compare metrics like average number of apples collected per episode, time to exit the maze, and how quickly the agent starts consistently hitting the full reward (all apples + exit). You’ll likely see the decaying ε run perform better once it shifts to exploitation.

内容的提问来源于stack exchange，提问作者Catarina Nogueira