What is sample efficiency?
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What is sample efficiency?
Sampling efficiency is a measure of the optimality of a sampling strategy. A more efficient sampling strategy requires fewer simulations and less computational time to reach a certain level of accuracy. The efficiency of a sampling strategy is highly related to its space-filling characteristics.
What is sample in reinforcement learning?
In reinforcement learning, importance sampling is a widely used method for evaluating an expectation under the distribution of data of one policy when the data has in fact been generated by a different policy.
Is PPO sample efficient?
The answer is Yes. As an on-policy algorithm, PPO solves the problem of sample efficiency by utilizing surrogate objectives to avoid the new policy changing too far from the old policy. The surrogate objective is the key feature of PPO since it both regularizes the policy update and enables the reuse of training data.
Why Q learning do not need Importance Sampling?
Q-learning is off-policy which means that we generate samples with a different policy than we try to optimize. Thus it should be impossible to estimate the expectation of the return for every state-action pair for the target policy by using samples generated with the behavior policy.
Why is sampling important in machine learning?
Sampling can be particularly useful with data sets that are too large to efficiently analyze in full — for example, in big data analytics applications or surveys. Identifying and analyzing a representative sample is more efficient and cost-effective than surveying the entirety of the data or population.
What is weighted importance sampling?
Weighted importance sampling is a generalisation of importance sampling. The basic idea is to compute a-posteriori a correction factor to the importance sampling estimates, based on sample weights accumulated during sampling.
Is PPO better than Trpo?
PPO is better than TRPO, matches performance of ACER on continuous control and is compatible with multi-output networks and RNNs. More similiar approaches: KFAC: KFAC does blockwise approximation to Fisher Information matrix(FIM) and approximate each block using a certain factorization.