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Pendulum dataset and benchmarks – Page 2 · SOTA2 Research

Research Datasets

Pendulum

Benchmarks

Task NameDataset NameSOTA MethodSort

Task NameDataset NameSOTA ResultTrendResults

Pendulum

7,231.1Log-Likelihood

6

Counterfactual Generation

Pendulum (test)

0.013MAE (Pendulum (p) | do(p))

6

Reinforcement Learning

Pendulum

80,240Steps (Mean)

6

Reinforcement Learning

Pendulum PD-C (test)

854Cumulative Reward

6

Continuous Control (Negative Reward)

Pendulum Pybullet

9,124.6Mean Return

6

Continuous Control (Positive Reward)

Pendulum Pybullet

9,043.3Return

6

Continuous Control (Negative Reward)

Pendulum Mujoco

8,132.1Mean Return

6

Continuous Control (Positive Reward)

Pendulum Mujoco

9,358.4Return

6

MCTS Aggregation Strategy Evaluation

Pendulum

0.75MRR

6

angular velocity decoding (prediction)

pendulum

0.727R^2

6

angular velocity decoding (smoothing)

pendulum

99.7R-squared

6

Reinforcement Learning

Pendulum

1,000Average Decisions

6

Continuous Control

Pendulum

1.12Action Repetition

6

Property Prediction

Pendulum

1,555.33Pendulum Angle

6

Imitation Learning

Pendulum

-179.6Mean Score

6

Pendulum swing-up

Pendulum D (test)

100Success Rate

5

Nonlinear control

Pendulum

3.405Evaluation Cost

5

Simulation-based Inference

Pendulum

0.287RMSE

5

Causal Representation Learning

Pendulum

98.94MIC

5

System Identification

Pendulum (test)

0.72Average MSE

5

Reinforcement Learning

Pendulum (train)

0.834Normalized Score

4

Pendulum swing-up

Pendulum D (train)

1.91MSE

4

Pendulum Control

Pendulum Experiment 3 1.0

730Cumulative Control Energy

4

Pendulum Control

Pendulum Experiment 2 1.0

552Cumulative Control Energy

4

Pendulum Control

Pendulum 1.0 (Experiment 1)

57Cumulative Control Energy

4

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Popular tasks

Reinforcement Learning Parameter Estimation Continuous Control Trajectory Learning Pendulum Control

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