Reinforcement Learning Best Practices

Practical guidance for reward design, hyperparameter tuning, debugging RL agents, reproducibility, and production deployment.

Reward Design

The reward function is the most important design choice in RL. A poorly designed reward leads to unintended behaviors:

• Keep it simple: Start with the simplest possible reward. Complex reward functions often have unintended loopholes the agent will exploit.
• Reward shaping: Add intermediate rewards to guide learning (e.g., reward for getting closer to the goal), but be careful - badly shaped rewards can lead to local optima.
• Sparse vs Dense: Dense rewards (every step) are easier to learn from. Sparse rewards (only at goal) are harder but less prone to reward hacking.
• Reward hacking: Agents will find unexpected ways to maximize reward. A cleaning robot rewarded for "not seeing mess" might learn to close its eyes.

Hyperparameter Tuning

Debugging RL

RL is notoriously hard to debug. Here is a systematic approach:

1. Verify the Environment

   Test with a random agent. Can a human solve it? Check that observations, actions, and rewards are correct. Use env.render() to visualize.

2. Sanity Check on Simple Tasks

   If your algorithm doesn't solve CartPole, it won't solve your complex task. Test on a known-solvable environment first.

3. Monitor Training Metrics

   Track episode reward, episode length, value loss, policy loss, entropy, and KL divergence. Use TensorBoard or Weights & Biases.

4. Check for Common Bugs

   Observation normalization, reward scaling, incorrect action spaces, off-by-one errors in done signals, and gradient clipping issues.

5. Ablation Studies

   Change one thing at a time. Compare against known baselines. Run multiple seeds.

Reproducibility

• Set seeds everywhere: Random seed, NumPy seed, PyTorch seed, CUDA seed, environment seed.
• Log everything: Hyperparameters, code version (git hash), environment details, training curves.
• Run multiple seeds: RL results vary wildly across seeds. Report mean and standard deviation over at least 3-5 seeds.
• Version your environments: Pin Gymnasium and library versions. Environment behavior can change between releases.

Algorithm Selection Guide

Frequently Asked Questions