However, not all tasks are easily or automatically reversible.

In practice, this learning process requires extensive human intervention.

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We prove that for the case of many dimensions, the superiority of the orthogonal transform can be accurately measured by a property we define called the charm of the kernel, and that orthogonal random features provide optimal (in terms of mean squared error) kernel estimators. Abstract: In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward.

We provide the first theoretical results which explain why orthogonal random features outperform unstructured on downstream tasks such as kernel ridge regression by showing that orthogonal random features provide kernel algorithms with better spectral properties than the previous state-of-the-art. Distributional reinforcement learning with quantile regression. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term return.

Abstract: We present an in-depth examination of the effectiveness of radial basis function kernel (beyond Gaussian) estimators based on orthogonal random feature maps.

We show that orthogonal estimators outperform state-of-the-art mechanisms that use iid sampling under weak conditions for tails of the associated Fourier distributions.

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Our results enable practitioners more generally to estimate the benefits from applying orthogonal transforms. Traditionally, reinforcement learning algorithms average over this randomness to estimate the value function.

In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean.

In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and reset policy, with the reset policy resetting the environment for a subsequent attempt.