39 lines
1.9 KiB
Markdown
39 lines
1.9 KiB
Markdown
# PrimAITE v3.2.0 Learning Benchmark
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## PrimAITE Dev Team
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### 2024-07-21
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---
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## 1 Introduction
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PrimAITE v3.2.0 was benchmarked automatically upon release. Learning rate metrics were captured to be referenced during system-level testing and user acceptance testing (UAT).
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The benchmarking process consists of running 5 training session using the same config file. Each session trains an agent for 1000 episodes, with each episode consisting of 128 steps.
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The total reward per episode from each session is captured. This is then used to calculate an caverage total reward per episode from the 5 individual sessions for smoothing. Finally, a 25-widow rolling average of the average total reward per session is calculated for further smoothing.
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## 2 System Information
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### 2.1 Python
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**Version:** 3.10.14 (main, Apr 6 2024, 18:45:05) [GCC 9.4.0]
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### 2.2 System
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- **OS:** Linux
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- **OS Version:** #76~20.04.1-Ubuntu SMP Thu Jun 13 18:00:23 UTC 2024
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- **Machine:** x86_64
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- **Processor:** x86_64
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### 2.3 CPU
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- **Physical Cores:** 2
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- **Total Cores:** 4
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- **Max Frequency:** 0.00Mhz
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### 2.4 Memory
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- **Total:** 15.62GB
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- **Swap Total:** 0.00B
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## 3 Stats
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- **Total Sessions:** 5
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- **Total Episodes:** 5005
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- **Total Steps:** 640000
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- **Av Session Duration (s):** 1691.5034
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- **Av Step Duration (s):** 0.0529
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- **Av Duration per 100 Steps per 10 Nodes (s):** 5.2859
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## 4 Graphs
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### 4.1 v3.2.0 Learning Benchmark Plot
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### 4.2 Learning Benchmark of Minor and Bugfix Releases for Major Version 3
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### 4.3 Performance of Minor and Bugfix Releases for Major Version 3
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