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. In many cases, the variability is not a bug but a natural consequence of how RL algorithms explore and learn. Below are the most common reasons for this behavior and practical steps you can take to achieve more stable, reproducible results.

1. Stochastic Environments and Policies

Most RL problems involve randomness:

• Random initial states or observations.
• Probabilistic transition dynamics (e.g., physics engines, game randomness).
• Exploration strategies such as ε‑greedy or Gaussian noise.

When any of these sources of randomness change between runs, the agent will naturally experience different trajectories, leading to divergent learning outcomes.

2. Random Seed Management

Even if the environment is deterministic, the underlying libraries (NumPy, PyTorch, TensorFlow, etc.) use random number generators for weight initialization, minibatch sampling, and exploration. If you don’t fix the seeds, each run starts from a different point in the parameter space.

What to do:

import random, numpy as np, torch
seed = 42
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
    torch.cuda.manual_seed_all(seed)

Set the same seed for every library you use, and also configure the environment (e.g., env.seed(seed)).

3. Non‑Deterministic GPU Operations

GPU kernels can be nondeterministic due to parallel reductions or atomic operations. This can cause tiny differences in gradients that amplify over many training steps.

Mitigation strategies:

• Enable deterministic mode (e.g., torch.backends.cudnn.deterministic = True and torch.backends.cudnn.benchmark = False).
• Run on CPU for debugging reproducibility, then switch back to GPU once you’re confident the algorithm works.

4. Hyper‑Parameter Sensitivity

RL algorithms are notoriously sensitive to learning rates, discount factors, batch sizes, and exploration schedules. Small changes can push the agent into a different region of the policy space.

Best practices:

• Perform a systematic hyper‑parameter sweep (grid search, random search, or Bayesian optimization).
• Log all hyper‑parameters alongside results for future reference.
• Use learning‑rate schedulers or adaptive optimizers (e.g., Adam) to reduce sensitivity.

5. Replay Buffer Sampling Variability

Off‑policy methods (DQN, SAC, TD3) rely on a replay buffer. The order in which experiences are sampled can affect gradient updates.

Tips:

• Use a fixed buffer size and start training only after the buffer is sufficiently populated.
• If possible, seed the random sampler that draws minibatches.

6. Early Stopping and Evaluation Frequency

When you evaluate the agent only at the end of training, you may miss the fact that performance peaked earlier and then degraded. Different runs can appear “better” or “worse” simply because they were stopped at different points.

Solution: Save checkpoints regularly and plot learning curves (reward vs. episode). Choose the checkpoint with the highest validation performance.

7. Overfitting to Random Seeds

If you tune hyper‑parameters on a single seed, the resulting configuration may overfit that particular random trajectory. When you change the seed, performance drops.

Recommendation: Evaluate each configuration on multiple seeds (e.g., 5–10) and report mean ± standard deviation.

Practical Checklist for Stable RL Training

1. Set and log a global random seed for all libraries and the environment.
2. Enable deterministic GPU operations if reproducibility is critical.
3. Document every hyper‑parameter and keep a version‑controlled config file.
4. Run a hyper‑parameter sweep and average results over several seeds.
5. Save model checkpoints and evaluation metrics at regular intervals.
6. Visualize learning curves to detect divergence early.
7. If variability persists, examine the environment for hidden stochasticity (e.g., random NPC behavior, physics jitter).

Conclusion

Seeing different outcomes on each training run is a common symptom of the inherent randomness in reinforcement learning pipelines. By controlling seeds, managing nondeterministic GPU behavior, carefully tuning hyper‑parameters, and evaluating across multiple seeds, you can dramatically reduce variance and gain confidence that your agent’s performance reflects true learning rather than luck.