Pendulum

Benchmarks

Task Name	Dataset Name	SOTA Result
Reinforcement Learning Control	Pendulum v1	Mean Score1,378.78	40
Reinforcement Learning	Pendulum	Avg Episode Reward-145.49	26
State Estimation	Pendulum	MAE0.7	21
Reinforcement Learning	Pendulum v1 (test)	Average Return-164.82	16
Sequential state estimation	Pendulum High observation noise	MSE8.881	14
Sequential state estimation	Pendulum High process noise	MSE0.1123	14
Sequential state estimation	Pendulum Default noise	MSE0.0835	14
Regression	Pendulum (test)	MSE0.0034	14
Reinforcement Learning	Pendulum v1	Average AUC (z-scored)2.18	13
Continuous Control	Pendulum	Robustness Gap0.72	12
Rollout prediction	Pendulum	Rollout MSE1.05	12
Continuous Control	Pendulum	Median Samples5.6	12
Continuous Control	Pendulum v1	Average Cumulative Reward-150.8	11
Regression	Pendulum	MSE3.32	11
Parameter Estimation	Pendulum 90cm	Length (m)1.07	9
Continuous Control	Pendulum Nonmarkov v1 (test)	AUC@T-556.9	9
Forecasting	Pendulum	MSE0.0687	9
Control	Pendulum v0	Median Samples21	9
Reinforcement Learning Surrogate Modeling	Pendulum (P) (test)	Reward Ratio95	8
Policy Ranking	Pendulum	Regret0.02	8
Transition model estimation	Pendulum discretized n = 10^5	Failure Rate0	8
Image Interpolation	Pendulum (test)	MSE1	8
Reinforcement Learning	Pendulum classical control (1M steps)	Return-133.42	8
Dynamical Identification	Pendulum Numerical	AUC99	7
Robotic Control	Pendulum v1	Local Optima Escape Rate89.2	7

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