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Benchmarks
Reinforcement Learning on MountainCar v0 (test)
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-101.72
Total Reward
Orthogonal DT
-203.9312
-177.3956
-150.86
-124.3244
Dec 14, 2020
Total Reward
Avg Steps
Updated 4d ago
Evaluation Results
Method
Method
Links
Total Reward
Avg Steps
Orthogonal DT
Optimization Criterion...
2020.12
-101.72
106.8
Closed-form policy
Source=Zhiqing Xiao
2020.12
-102.61
54.7
Soft Q Networks
Source=Keavnn
2020.12
-104.58
31,079.2
Tabular SARSA
Source=Amit
2020.12
-105.99
381.5
Oblique DT
Optimization Criterion...
2020.12
-106.02
46.8
Double Deep Q Network
Source=Colin M
2020.12
-107.83
46,681.6
Deep Q Network
Source=Harshit Singh
2020.12
-108.85
984,160.3
Orthogonal DT
Optimization Criterion...
2020.12
-116.68
35.6
Nonlinear DT (Open loop)
Source=Dhebar et al.
2020.12
-128.87
66.8
Oblique DT
Optimization Criterion...
2020.12
-200
0
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