Disagreement Regularized Imitation Learning

The basic idea is also very simple by using the idea of lowering in statistical learning, using the variance of the results of several policy functions driven by expert data on a state (called disagrement in predictions), as costs, and then using the gradient learning strategy. In addition, it is also combined with BC`s Gradient Direct Learning. This article claims to solve BC`s kovariat displacement and proves a linear bond. DRIL requires a pro-workout set model and a pro-trained behavioral cloning model. Note that is the full path to the top-level directory to the rl_baseline_zoo repository. GitHub is home to more than 50 million developers who work together to host and verify code, manage projects, and create software together. If nothing happens, download GitHub Desktop and try again. We thank Ilya Kostrikov for creating this "repo" on which our code base is based. . "stable-baselines", "rl-baselines-zoo", "baselines", "gym", "pytorch", "pybullet" 1。 The first experiment was to check the fixation of the reret in the tabular CDM.

In this case, the distribution of directives can be calculated with a separate beta distribution for each status in the example. The reward combines variance and s, the probability after the test. --default_experiment_params the standard parameters we use in THEIL tests and two options: atari and continous-control This article uses actor-critic advantage (A2C) to update the policy gradient. 3。 The third experiment was the continuous control carried out on the PyBullet, the engine that was able to replace MuJoCo. But in a way, there is no comparison with GALL. We provide a Python script to generate expert data from pro-trained models with the "rl-baselines-zoo" repository. Click on "Here" to see all pre-trained agents and their respective information. Replace with the name of the pre-trained agent environment for which you want to collect expert data.

To form a DRIL model, run the following command. Note that the following command first checks whether the behavioral clone model and the set model are trained, if not, the script automatically drags both the set model and the behavioral clone model. Millions of developers and businesses develop, ship, and wait for their software on GitHub, the world`s largest and most advanced development platform. Disagreement-Regularized Imitation Learning (Anonymous). Code for training the models described in the article "Disagreement-Regularized Imitation Learning" by Kianté Brantley, Wen Sun and Mikael Henaff. Even in training, uncertainty has yielded costs as well as rewards. The figure is not too understandable, I would like to say that BC "bad pessimistic performance with some attempts of very great regret, if when using fewer demonstrations" but this article suggested that the method "regrets little all attempts". There are many ways to appreciate this post-test, this article uses the overall method.

The variance of the clip is actually used as a cost, as follows: This paper gives a form of reward based on expert data from a statistical point of view. This recent work of learning imitation is reminiscent of the study of intensive learning, and many of these works also have some intuitive ways to measure the state of the novel, to learn from one another. On the tabular CDM, the author proves that the fixation of regrets of the algorithm is linear by defining the coefficient. One of them concerns the environment and the distribution of data that has been tested by experts. The algorithm in this article is superior to BC if smaller than. In particular, the author proves in the CDM below that regrets are related and BEFORE JESUS CHRIST. The principles of the strategy to be learned from the algorithm are similar to those described in the previous article of the SQIL. The first is to show as close as possible to the distribution of experts, and the second is to return to the distribution of experts after destipping from the data. Detailed derivation or reading of the document (the appendix also contains the derivation).

In general, the method of this work is quite new, there are theoretical analyses, although somewhat far-fetched, but it is not without theoretical contribution. The experience has been very thorough. 2。 The second experiment was the Atari benchmark, which compared GAIL, and the PERFORMANCE of the GAIL when entering the image was really bad. The last function of imitator with fixed reward as well as the previous two were recorded at three. This note is written in DRIL. This article is currently anonymous in casting ICLR, with a recent score of 886. 2) The second is to compare the uncertainty estimation method and replace together with an MC dropout (see document). After the models are formed, the results are stored in a folder called trained_results. . .


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