MOD-001 // ENVIRONMENT

Multi-Agent Environment

Discrete-time partially observable environment hosting N heterogeneous agents. Supports configurable adversarial injection ratios and stochastic reward structures per episode.

{running ? "RUNNING" : done ? "COMPLETE" : "IDLE"}

gym v0.26.2

MOD-002 // TRUST ENGINE

Trust Calibration Engine

Bayesian trust scoring module that maintains per-agent belief distributions. Updates posteriors using observed action-outcome consistency.

CALIBRATING

TCE v1.1.4

MOD-003 // ADV DETECTION

Adversarial Detection Layer

Anomaly-based detector using temporal divergence scoring across agent action histories. Flags Byzantine agents via KL-divergence threshold on expected vs observed policy distributions.

0 ? "var(--red)" : "var(--green)" }}>

0 ? { background: "var(--red)" } : {}} /> {adversarialCount > 0 ? `${adversarialCount} THREAT${adversarialCount > 1 ? "S" : ""}` : "CLEAR"}

ADL v2.0.1

MOD-004 // RL OPTIMIZER

Reinforcement Learning Optimizer

Proximal Policy Optimization (PPO) with trust-weighted reward shaping. Policy gradient updates incorporate adversarial penalty terms.

TRAINING

PPO v3.2.0

MOD-005 // GPU COMPUTE

H100 GPU Compute Fabric

Underlying hardware substrate orchestrating 1.2M CUDA cores. Dynamic load balancing across N nodes with real-time thermal management.

4 NODES ONLINE

H100-v2

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