What should I do, reinforcement learning agent gives different result on every train?

Why Does My Reinforcement Learning Agent Produce Different Results Every Training Run?

It’s frustrating when a reinforcement learning (RL) agent behaves inconsistently across training sessions. One run might achieve near‑optimal performance, while the next falls short or diverges entirely. This variability is a common symptom of hidden nondeterminism in the training pipeline. Below we explore the typical culprits, practical steps to regain reproducibility, and a checklist to debug your RL experiments.

1. Sources of Nondeterminism in RL

• Random Seed Management: Many libraries (NumPy, PyTorch, TensorFlow, OpenAI Gym) maintain separate RNG states. Forgetting to set or synchronize all seeds leads to different environment dynamics and weight initializations.
• Environment Stochasticity: Some environments (e.g., Atari with sticky actions, MuJoCo with random initial states) intentionally inject randomness. If the seed isn’t fixed, each episode starts from a different state distribution.
• Parallelism & Asynchronous Updates: Algorithms like A3C, PPO with multi‑process workers, or distributed replay buffers can cause race conditions. The order of gradient updates may vary, producing divergent learning trajectories.
• GPU Non‑Determinism: Certain CUDA operations (e.g., atomic adds, nondeterministic reductions) yield slight numerical differences that can amplify over many training steps.
• External Libraries: Third‑party components (e.g., OpenCV for image preprocessing, random data augmentations) may have their own RNGs that need seeding.

2. How to Enforce Deterministic Training

1. Set All Random Seeds

   import random, numpy as np, torch
   random.seed(42)
   np.random.seed(42)
   torch.manual_seed(42)
   torch.cuda.manual_seed_all(42)
       

   Also pass the seed to the environment: env.seed(42) or gym.make(..., seed=42).

2. Disable Parallelism (or Control It)

   If you use multi‑process workers, set num_workers=0 for debugging. When you need parallelism, use deterministic communication primitives (e.g., torch.backends.cudnn.deterministic = True).

3. Force Deterministic CUDA Operations

   torch.backends.cudnn.deterministic = True
   torch.backends.cudnn.benchmark = False
       

   Be aware this may slow down training.

4. Fix Environment Randomness

   Initialize the environment with a fixed seed and, if possible, disable any built‑in stochastic wrappers (e.g., StickyActionWrapper).

5. Log and Freeze Hyperparameters

   Store the exact configuration (learning rate, discount factor, network architecture) in a JSON or YAML file and load it at the start of each run.

6. Version Control Dependencies

   Record the exact library versions (e.g., torch==2.1.0, gym==0.26.2) and use a virtual environment or Docker container to guarantee the same runtime.

3. Debugging Checklist

• ✅ All seeds set before any library import that uses randomness.
• ✅ No hidden RNGs in data augmentations or custom wrappers.
• ✅ Deterministic flag enabled for CUDA (if using GPUs).
• ✅ Single‑process training for baseline reproducibility.
• ✅ Environment reset with the same seed each episode.
• ✅ Consistent batch ordering in replay buffers (shuffle disabled for debugging).
• ✅ No nondeterministic operations like torch.nn.functional.dropout during evaluation.

4. When Perfect Reproducibility Isn’t Possible

Even after tightening every knob, some RL algorithms (especially those relying on exploration noise or stochastic policies) will still exhibit minor variance. In such cases:

• Run multiple seeds (e.g., 5–10) and report mean ± standard deviation.
• Use statistical tests (t‑test, bootstrap) to compare algorithms.
• Focus on learning curves rather than a single final score.

5. Takeaway

Inconsistent results across training runs are usually a symptom of hidden randomness or nondeterministic computation. By systematically seeding every component, controlling parallelism, and logging your environment, you can dramatically reduce variance and gain confidence that performance differences stem from algorithmic changes—not accidental noise.

Implement the steps above, run a few sanity checks, and you’ll soon see your RL experiments become far more predictable—and ultimately more trustworthy.