This means that the above sequence of length 60 would have taken 120 bits to represent it. If you hadn’t used any entropy coding, you would have assigned 2 bits to each character. So if we go ahead with the above representation, it will take 51 + 3 x 30 = 141 bits to represent the data stream. Now consider the first 60 alphabets in the same data stream: SSCAAACCCKKKKAKKKKKKKKCCCAKKKKSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS If you substitute these values, you will see that it optimally compresses the above sequence to 51 bits. So we go ahead and build an entropy coder using Huffman coding to get the following representation: S = 000 (3 bits) You can see that the letter ‘K’ appears a lot. Okay, let’s consider the first 30 alphabets in our data stream: SSCAAACCCKKKKAKKKKKKKKCCCAKKKK Nothing clears up a discussion like a concrete example. I’m not sure what you are talking about! Show me an example. Do we end up suffering in terms of compression by doing this? How do we measure the loss in quality? Since you cannot wait forever, you just wait for the first ‘n’ alphabets and build your entropy coder hoping that the rest of the data will adhere to this distribution.
#Relative entropy full
But what if you don’t have access to all the data? How do you know what alphabet appears most frequently if you can’t access the full data? The problem now is that you cannot know for sure if you have chosen the best possible representation. So you go ahead and build your nifty entropy coder to take care of all this. Let’s say we have a stream of English alphabets coming in, and you want to store them in the best possible way by consuming the least amount of space. So coming to the topic at hand, let’s continue our discussion on entropy coding. If not, you may want to quickly read through my previous blog post. If you are familiar with the basics of entropy coding, you should be fine. We present three different variants of our algorithm, designed to be suitable for a wide variety of real world robot learning tasks and evaluate our algorithms in two real robot learning scenarios as well as several simulations and comparisons.In this blog post, we will be using a bit of background from my previous blog post.
#Relative entropy update
In order to efficiently share experience with all sub-policies, also called inter-policy learning, we treat these sub-policies as latent variables which allows for distribution of the update information between the sub-policies. We define the problem of learning sub-policies in continuous state action spaces as finding a hierarchical policy that is composed of a high-level gating policy to select the low-level sub-policies for execution by the agent. Real world settings are challenging due to large and continuous state-action spaces that are prohibitive for exhaustive sampling methods. However, this concept has only been partially explored for real world settings and complete methods, derived from first principles, are needed. Such task structures can be exploited by incorporating hierarchical policies that consist of gating networks and sub-policies.
Many reinforcement learning (RL) tasks, especially in robotics, consist of multiple sub-tasks that are strongly structured. Hierarchical Relative Entropy Policy SearchĬhristian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters 17(93):1−50, 2016.
0 kommentar(er)
