ReinΩlytix™
adaptive RL & Bayesian meta‑learning
Empowering AI to Continuously Learn, Adapt, and Optimize in Dynamic Environments
adaptability 1.5× – 5×
data efficiency 40% – 60%
decision optimization 20% – 40%
Environment:
Algorithm:
Adaptability depth:
80%
OpenAI Gym / MuJoCo
convergence speed (episodes to optimal reward)
data efficiency (reward vs environment steps)
decision‑making efficiency (normalized)
multi‑agent coordination gain
model drift reduction (reward stability)
reward path efficiency (5× improvement)
multi‑dimensional performance
few‑shot adaptation (tasks mastered)
benchmark details – Walker2D (MuJoCo) with PPO
| metric |
traditional RL |
ReinΩlytix |
improvement |
Benchmarked on OpenAI Gym, MuJoCo, DeepMind Control Suite, CARLA
GPU: NVIDIA A100, CPU: Intel Xeon
⚡ ReinΩlytix advantage
- 1.5× – 5× faster convergence in dynamic envs
- 40% – 60% fewer samples (few‑shot learning)
- 20% – 40% better decision efficiency
- 50% model drift reduction, 70% less catastrophic forgetting
traditional AI baseline
- fails in unseen environments, requires retraining
- high GPU/CPU demand for RL
- struggles with non‑Euclidean reward spaces
- performance degrades over time (drift)
Ω‑Adaptive RL
Dynamic adaptation ≤200ms, 70% less catastrophic forgetting.
Non‑Parametric Bayesian
40‑60% fewer labels, +30% generalization accuracy.
Non‑Euclidean Reward
5× path efficiency, 15‑25% reward increase.
optimized architecture
Pyro, TensorFlow Probability
PettingZoo, SMAC (MARL)
CARLA, RLBench
✔ Hierarchical RL (3× precision), Bayesian policy optimization (40% less uncertainty), self‑learning reward adaptation.
Deep Bayesian Networks
Targeting 50% higher predictive accuracy.
Hierarchical RL
30% faster learning via decision decomposition.
Federated RL
Privacy‑preserving multi‑agent collaboration.