基于DQN的简易赛车游戏训练性能波动问题咨询

First, let's recap your setup to align on the context:

• State Input: Vehicle x-coordinate, obstacle x & y coordinates
• Reward Mechanism: Positive reward for avoiding obstacles, penalty for wall collisions/obstacle hits
• NN Architecture: 2 hidden layers (100 units with ReLU → 50 units with ReLU)
• Optimizer: Adam (learning rate 1e-6)
• Loss: Mean Squared Error ((Y - Y_var)^2)
• Target Network Update: Every 1000 steps
• Epsilon Decay: Starts at 1, decays by 0.99 until reaching 0

Performance fluctuations are super common in DQN training—let’s break down the most likely culprits and tailored fixes for your setup:

1. Epsilon Decay Schedule Problems #

Your exponential epsilon decay (0.99 per step) might be throwing off the exploration-exploitation balance:

• If epsilon drops too fast, the agent could lock into suboptimal behaviors early before exploring enough of the state space.
• If it decays too slow, the agent keeps taking random actions even after it should be leaning on learned policies, leading to noisy, inconsistent performance.

Fixes:

• Try a linear decay instead: Drop epsilon from 1 to 0.1 over 100k steps, then keep it at 0.1 indefinitely (some ongoing exploration prevents overfitting to stale policies).
• Adjust the decay rate: If 0.99 makes epsilon plummet too quickly (e.g., it hits 0.001 after ~700 steps), switch to a slower rate like 0.999 to let the agent explore longer.

2. Target Network Update Frequency #

Updating the target network every 1000 steps might be too infrequent. DQN depends on the target network to provide stable Q-value targets—if it’s outdated, loss can swing wildly, causing performance jumps.

Fixes:

• Reduce the update interval to 500 or 200 steps and check for improved stability.
• Switch to soft target updates instead of hard ones: Update the target network weights by a small factor (e.g., tau=0.001) every step with target_weights = tau * current_weights + (1 - tau) * target_weights. This keeps targets far smoother over time.

3. Reward Signal Design Gaps #

Your binary reward system (reward for avoidance, penalty for crash) is sparse, which can make it hard for the agent to learn consistent intermediate behaviors. If feedback only comes during crashes or successful avoids, the agent misses subtle cues that lead to steady performance.

Fixes:

• Add a tiny positive reward every frame the agent stays alive (e.g., +1 per step). This encourages survival, which provides a steady stream of learning signals.
• Tune penalty magnitude: If the crash penalty is overly harsh (e.g., -100), it can spike loss and destabilize training. Try a moderate penalty like -10 or -20, and make the obstacle-avoidance reward proportional (e.g., +5 per obstacle dodged).

4. Neural Network & Optimizer Hyperparameter Issues #

Your network size is reasonable, but a learning rate of 1e-6 is extremely low. A too-small learning rate means the agent learns at a glacial pace, leading to slow convergence and volatile performance as policies take forever to update.

Fixes:

• Bump the learning rate to a standard DQN range: Start with 1e-4 or 5e-5, then lower it if you see loss exploding.
• Add batch normalization to hidden layers—this stabilizes training by normalizing activations, which can cut down on performance swings.
• If you suspect underfitting, try increasing hidden layer sizes (e.g., 256 → 128 units) or adding a third layer, but prioritize tuning the learning rate first.

5. Experience Replay (Critical, Even If You Didn’t Mention It) #

DQN relies on experience replay to break correlations between consecutive states. If you’re not using it, or your buffer is too small, that’s a major source of instability.

Quick Checklist:

• Use a replay buffer of sufficient size (100k to 1 million transitions).
• Sample batches randomly from the buffer (avoid sequential sampling).
• Wait until the buffer is at least half-full before starting training—this ensures you’re learning from a diverse set of experiences.

6. Gradient Clipping (Easy Stability Win) #

You’re using standard MSE loss and Adam, which is fine, but missing gradient clipping can let large weight updates destabilize the network.

Fix:

• Add gradient clipping (e.g., clip gradients to a norm of 1.0) when applying optimizer steps. This is a classic DQN trick to keep training consistent.

Try these adjustments one at a time (so you can isolate what’s working) and track performance metrics across training runs. Small tweaks to these hyperparameters often make a huge difference in stability!

内容的提问来源于stack exchange，提问作者Dane Lee