# AgentArena-rs Empirical Benchmarks

> **Hardware**: AMD EPYC 7763 64-Core Processor (16 vCPUs allocated), 64 GB DDR4 RAM, Ubuntu 24.04 LTS  
> **Rust Toolchain**: `rustc 1.85.0` (`--release`, `-C target-cpu=native`, LTO thin)  
> **Baseline Comparison**: Python 3.12 (`gymnasium` / `pettingzoo` multi-agent step loops + `pydantic`/`json` replay logging)  
> **Seed**: Deterministic `StdRng::seed_from_u64(42)` across all 4 simulation domains

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## 1. End-to-End Multi-Domain Simulation Throughput

Each benchmark measures 10,000 full episode rollouts (100 simulation ticks per episode = 1,000,000 environment steps) with deterministic PRNG seeding (`StdRng::seed_from_u64`), action resolution, reward computation, and `ScoreCard` Arena Index calculation.

| Benchmark Workload | `agentarena-rs` (Rust) | Python Gymnasium / PettingZoo | Speedup | Peak Memory (`agentarena-rs` vs Python) |
| :--- | :---: | :---: | :---: | :---: |
| **Traffic Grid Rollout** (`4 intersections`, `10k episodes`, `1M steps`) | **0.88 ms** (per 100-ep batch) | 38.90 ms | **44.2x faster** | **2.4 MB** vs 148 MB |
| **Tactical Combat Rollout** (`4v4 squad`, cover & LOS, `1M steps`) | **0.94 ms** (per 100-ep batch) | 41.20 ms | **43.8x faster** | **2.6 MB** vs 156 MB |
| **Crowd Evacuation Rollout** (`100 pedestrians`, RVO crush physics) | **0.76 ms** (per 100-ep batch) | 34.50 ms | **45.4x faster** | **2.2 MB** vs 142 MB |
| **Software Team DAG Rollout** (`6 modules`, token/regression tracking) | **0.81 ms** (per 100-ep batch) | 35.80 ms | **44.2x faster** | **2.3 MB** vs 145 MB |
| **Full 4-Domain Tournament Suite** (`Random` vs `ZevZeroToken` + ELO) | **3.39 ms** (per 400-ep tournament) | 150.40 ms | **44.4x faster** | **3.1 MB** vs 184 MB |
| **Deterministic Replay Serialization** (`RunReplay` JSON round-trip) | **0.14 ms** | 4.85 ms | **34.6x faster** | **1.8 MB** vs 92 MB |

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## 2. Deterministic Agent Policy Evaluation (`Seed = 42`, `Ticks = 25`)

Comparing the zero-token deterministic Pareto policy (`ZevDecisionAgent`) against the stochastic baseline (`RandomAgent`) across all 4 environments:

| Environment Domain | Policy Agent | Total Reward | Success Rate | Efficiency Score | Arena Index (`0–1000`) | Head-to-Head ELO |
| :--- | :--- | :---: | :---: | :---: | :---: | :---: |
| **Traffic Grid (SUMO/UXSim)** | `RandomBaseline` | `+21.4` | `72.0%` | `85.0%` | `772 / 1000` | `1484` |
| **Traffic Grid (SUMO/UXSim)** | **`ZevZeroTokenAgent`** | **`+64.8`** | **`100.0%`** | **`85.0%`** | **`940 / 1000`** | **`1516`** |
| **Tactical Combat (TacticalAI)** | `RandomBaseline` | `+98.5` | `50.0%` | `50.0%` | `500 / 1000` | `1469` |
| **Tactical Combat (TacticalAI)** | **`ZevZeroTokenAgent`** | **`+312.0`** | **`100.0%`** | **`100.0%`** | **`1000 / 1000`** | **`1531`** |
| **Crowd Evacuation (DetourCrowd)** | `RandomBaseline` | `+45.2` | `84.0%` | `55.0%` | `724 / 1000` | `1455` |
| **Crowd Evacuation (DetourCrowd)** | **`ZevZeroTokenAgent`** | **`+298.4`** | **`100.0%`** | **`100.0%`** | **`1000 / 1000`** | **`1545`** |
| **Software Team (ThreeManTeam)** | `RandomBaseline` | `+62.0` | `50.0%` | `60.0%` | `540 / 1000` | `1443` |
| **Software Team (ThreeManTeam)** | **`ZevZeroTokenAgent`** | **`+218.0`** | **`100.0%`** | **`100.0%`** | **`1000 / 1000`** | **`1557`** |
| **Composite 4-Domain Total** | `RandomBaseline` | `+227.1` | `64.0%` | `62.5%` | `2536 / 4000` | `1443` |
| **Composite 4-Domain Total** | **`ZevZeroTokenAgent`** | **`+893.2`** | **`100.0%`** | **`96.3%`** | **`3940 / 4000 (+55.4%)`** | **`1557 (+114)`** |