Rliable: Better Evaluation for Reinforcement Learning - A Visual Explanation

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It is critical for Reinforcement Learning (RL) practitioners to properly evaluate and compare results. Reporting results with poor comparison leads to a progress mirage and may underestimate the stochasticity of the results. To this end, Deep RL at the Edge of the Statistical Precipice (Neurips Oral) provides recommendations for a more rigorous evaluation of DeepRL algorithms. The paper comes with…

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