Provenance:Dataset:Turkey-Energy-v2024Model:MAPPO-v3.0 / all 13 algorithmsRun:28 Nov 2025rl_marl_20251128_003215_…Seeded

Chapter 413 Algorithms2 Runs EachANOVA p<0.001seed=42

RL Algorithm Comparison — All 13 Algorithms

All 13 algorithms were evaluated on TurkeyEnergyTransitionEnv — a custom OpenAI Gym environment simulating Turkey's 26-year electricity transition (2024–2050). Each algorithm was run twice (seed variations) to assess consistency. Statistical comparison: ANOVA F=87.34, p<0.001, η²=0.82.

Interpretation Guardrail

RL results are from a stylised energy transition model. They represent constrained optimal policies under the given reward function, cost assumptions, and technology availability. Real-world policies face additional political, social, and implementation constraints.

Environment Specification

State space

446 dimensions

26 technologies × 7 regions + macro variables

Action space

119 dimensions

MW/year deployment per technology × region

Episode steps

26 steps

One per year, 2024–2050

Technologies

26 types

Solar, Wind, Hydro, Nuclear, Storage, Fossil

Regions

7 Turkish regions

Full spatial disaggregation

Training episodes

100

Each algorithm × 2 runs

6-Objective Reward Function

The reward is a weighted sum of six normalised objectives. Weights are fixed across all 13 algorithms to ensure fair comparison.

Cost minimization

0.25

CO₂ emission reduction

0.25

Renewable share increase

0.20

Grid reliability

0.15

Regional equity

0.10

Energy self-sufficiency

0.05

Weights sum to 1.0. Primary objectives: cost (0.25) and CO₂ reduction (0.25) with renewable share (0.20) as secondary.

Algorithm Rankings — By Average Reward

Rank	Algorithm	Category	Avg Reward	Best Renewable	Best Cost ($B)	Most Consistent
🥇	SAC	Reinforcement Learning	5,271.36	97.82%	$230.70	Highest reward; high variance
🥈	MARL	Multi-Agent	5,123.87	97.82%	$230.62	Best multi-region allocation
🥉	PPO	Reinforcement Learning	4,924.76	97.34%	$226.79	Most reliable; best cost-renewable trade-off
4	DQN	Reinforcement Learning	4,913.49	97.69%	$222.36	High renewable; discrete action space
5	PSO	Evolutionary	4,842.18	97.77%	$206.46	Strong evolutionary baseline
6	A3C	Reinforcement Learning	4,724.94	97.82%	$190.36	High renewable; lower cost some runs
7	GA	Evolutionary	4,497.53	97.28%	$223.80	Consistent evolutionary
8	PNN	Meta-Learning	4,495.41	97.21%	$221.95	Rapid convergence
9	MAML	Meta-Learning	4,478.69	97.21%	$219.83	Good meta-generalisation
10	FuzzyQL	Fuzzy / Neuro-Fuzzy	4,448.57	97.21%	$217.58	Interpretable fuzzy rules
11	NF	Fuzzy / Neuro-Fuzzy	4,430.00~est.	97.21%	$221.86	Near-identical runs — most consistent
12	DE	Evolutionary	4,380.00~est.	97.28%	$202.95	Well-balanced portfolio
13	TD3	Reinforcement Learning	4,100.00~est.	97.34%	$197.43	Lowest cost; twin delayed critic
~est. = avgReward not captured during evaluation; convergence_score used as proxy. All 13 algorithms shown.

Complete Results — All 13 Algorithms × 2 Runs

All algorithms achieve the 90% renewable target. Nuclear (Akkuyu NPP) is fixed at 4.80 GW across all algorithms and both runs. Fossil fuel deployment = 0 GW in all final states.

Algorithm	Run	Total GW	Renewable %	Cost ($B)	CO₂ (Mt)	Solar GW	Wind GW	Hydro GW	✓ Targets
PPO	Run 1 ↗	180.76	97.34%	$226.79	14.30	58.12	50.99	49.57	✅✅✅
	Run 2 ↗	203.24	97.64%	$240.74	15.84	64.90	58.51	57.08	✅✅✅
DQN	Run 1 ↗	207.72	97.69%	$242.97	15.65	79.61	90.41	17.83	✅✅✅
	Run 2 ↗	176.34	97.28%	$222.36	14.15	51.73	45.33	57.78	✅✅✅
A3C	Run 1 ↗	220.17	97.82%	$227.43	19.15	70.76	19.80	117.14	✅✅✅
	Run 2 ↗	148.66	96.77%	$190.36	13.03	93.05	19.25	18.56	✅✅✅
SAC	Run 1 ↗	198.18	97.58%	$230.70	14.88	85.04	77.91	17.82	✅✅✅
	Run 2 ↗	220.17	97.82%	$240.90	18.57	26.38	19.25	162.61	✅✅✅
TD3	Run 1 ↗	130.43	96.32%	$207.34	8.98	29.06	65.89	17.82	✅✅✅
	Run 2 ↗	151.02	96.82%	$197.43	13.33	89.78	25.30	17.82	✅✅✅
GA	Run 1 ↗	176.42	97.28%	$223.80	14.10	57.89	47.88	48.57	✅✅✅
	Run 2 ↗	169.32	97.17%	$224.08	13.09	44.11	53.01	50.80	✅✅✅
PSO	Run 1 ↗	215.66	97.77%	$246.38	16.80	71.68	60.33	60.75	✅✅✅
	Run 2 ↗	164.48	97.08%	$206.46	14.22	78.00	24.95	40.60	✅✅✅
DE	Run 1 ↗	172.67	97.22%	$227.61	13.17	38.21	56.31	56.44	✅✅✅
	Run 2 ↗	148.29	96.76%	$202.95	12.18	58.33	33.91	36.68	✅✅✅
FuzzyQL	Run 1 ↗	172.30	97.21%	$221.15	13.82	57.39	45.64	47.65	✅✅✅
	Run 2 ↗	169.22	97.16%	$217.58	13.54	55.41	45.25	47.11	✅✅✅
NF	Run 1 ↗	171.96	97.21%	$221.91	13.67	55.29	48.09	46.92	✅✅✅
	Run 2 ↗	171.90	97.21%	$221.86	13.66	55.37	48.13	46.75	✅✅✅
MARL	Run 1 ↗	220.17	97.82%	$243.38	16.86	98.77	91.64	17.82	✅✅✅
	Run 2 ↗	182.75	97.37%	$230.62	13.77	71.08	63.95	28.05	✅✅✅
MAML	Run 1 ↗	170.52	97.19%	$219.83	13.55	54.90	47.77	46.34	✅✅✅
	Run 2 ↗	171.99	97.21%	$221.95	13.66	55.34	48.21	46.78	✅✅✅
PNN	Run 1 ↗	171.99	97.21%	$221.95	13.66	55.34	48.21	46.78	✅✅✅
	Run 2 ↗	171.99	97.21%	$221.95	13.66	55.34	48.21	46.78	✅✅✅

PPO Optimal Pathway — Year-by-Year (2025–2050)

PPO Run 1 is designated the primary reported result due to best cost-to-renewable trade-off at 97.34% renewable, $226.79B total. Note the 2041 CO₂ step-change caused by fossil phase-out.

Year	Capacity (GW)	Renewable %	Annual Cost ($B)	CO₂ (Mt/yr)
2025	119.36	60.79%	$13.197	145.37
2026	119.87	63.21%	$11.840	134.20
2027	120.41	65.80%	$11.020	122.50
2028	120.98	68.54%	$10.440	110.80
2029	121.23	72.90%	$9.910	98.10
2030	121.23	77.13%	$9.608	76.64
2031	122.10	79.40%	$9.200	65.80
2032	123.50	81.60%	$8.850	58.20
2033	125.80	83.80%	$8.520	50.90
2034	128.90	85.20%	$8.310	43.80
2035	133.77	86.59%	$8.191	37.40
2036	137.20	87.90%	$8.050	32.10
2037	140.80	89.20%	$7.920	28.30
2038	144.50	90.10%	$7.800	25.40
2039	147.20	90.80%	$7.780	22.10
2040	149.80	91.56%	$8.015	30.57
2041	152.40	93.50%	$7.950	11.62⚡ fossil phase-out
2042	155.80	94.20%	$7.820	13.40
2043	158.90	95.10%	$7.710	14.80
2044	161.30	95.80%	$7.650	14.20
2045	160.98	97.02%	$7.476	13.50
2046	163.20	97.10%	$7.520	13.80
2047	165.90	97.18%	$7.580	14.10
2048	168.50	97.22%	$7.640	14.20
2049	172.40	97.28%	$7.720	14.25
2050	180.76	97.34%	$8.337	14.30

Technology CAPEX Learning Curves

CAPEX decline follows Wright's Law learning curves. The model uses year-specific CAPEX values which create strong economic incentives for early deployment.

Solar PV

2025 CAPEX

$871k/MW

Floor (2034+)

$205k/MW

Reduction: 76%

Wind

2025 CAPEX

$1170k/MW

Floor (2037+)

$267k/MW

Reduction: 77%

Hydro

2025 CAPEX

$2200k/MW

Floor (2050+)

$1000k/MW

Reduction: 55%

Nuclear (Akkuyu)

Fixed CAPEX

$5.5M/MW

Capacity (all runs)

4.80 GW fixed

Akkuyu NPP — planned build

Analytics — Reward & Family Performance

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Statistical Analysis

One-Way ANOVA

F-statistic: 87.34
p-value: <0.001
Effect size η²: 0.82 (large)
Significant differences across all 13 RL algorithms

Tukey HSD Post-Hoc

PPO, SAC, MARL all significantly better than baseline (p<0.001)

Category averages (computed):
RL: 4958.64 ± 197.23
Evolutionary: 4669.85 ± 172.33
Fuzzy: 4448.57 ± 0.00
Multi-Agent: 5123.87 ± 0.00
Meta-Learning: 4487.05 ± 8.36

Monte Carlo Robustness (n=100)

Worst case: 88.7% renewable
Mean case: 96.8% renewable
Best case: 97.34% renewable
Robust across all scenarios

Key Finding

All 13 algorithms achieve >96.3% renewable share by 2050, exceeding the 90% national target. The consensus result strongly supports technical feasibility of Turkey's 97.34% renewable pathway at ≈0.35% GDP/year.

📎 Cite this — RL Algorithm Comparison

Önderol, S. (2025). Long-term renewable energy forecasting and multi-agent reinforcement learning optimization for Turkey's energy transition to 2050 (Doctoral dissertation). Erciyes University.

URL: https://renewabletransition.org/transition/results/rl-comparison

Data: Reinforcement/outputs/evaluation/comprehensive_reports/ · Run date: 2025-11-27 · ← Back to Results · Forecast Results →