How does an average-reward RL agent compare to a well-tuned (s, S) reorder-point policy? We simulate 10,000 days of single-echelon inventory under seasonal demand — ARPPO achieves 23% lower holding costs and 31% fewer stockouts without any manual parameter tuning.
ARPPO vs Reorder-Point Policies: A Simulation Study of Adaptive Inventory Control Read More »
PPO’s discount factor gamma encodes strong assumptions about the planning horizon that don’t hold in continuous operational environments. ARPPO eliminates this dependency entirely, making reward shaping and policy tuning dramatically simpler in practice.
Why ARPPO Is Less Sensitive to Hyperparameters Than PPO Read More »
Standard PPO fails on real-world tasks because raw reward signals destabilize value learning. ARPPO solves this with Welford online statistics — numerically stable, constant-memory reward normalization that adapts continuously across training. Combined with an annealed value shrink curriculum, this two-layer stability system makes ARPPO dramatically more reliable than PPO for enterprise deployments.
Reward Normalization Done Right: How ARPPO Uses Welford Statistics for Stable Training Read More »
Running a single environment one step at a time is the bottleneck that holds most RL systems back. Vectorized environments — running N copies of the same problem in parallel — solve this. Here is how LARA implements them, and why it matters for business AI.
Vectorized Environments: How Parallel Sampling Makes Reinforcement Learning Scale Read More »
In reinforcement learning, more training is not always better. KL divergence provides the principled mechanism that tells ARPPO when enough is enough — protecting policy stability without sacrificing sample efficiency.
KL Divergence and Epochs: Why ARPPO Knows When to Stop Learning Read More »
