It's a very complex task. The price of bitcoin could be affected by NEWs(political desicions, network upgrades, other cryptocurrencies), whales buying and selling crypto, network hashrate.
You can try to search for predictions after lets say after 5 minutes, based on the data inside the orderbook on Exchanges. The market is very volatile, you can make ...
It should be noted that the selection of $\alpha$ is a classic problem in stochastic approximation, rather than a specific problem in RL. Once you know this it will be clear.
What is stochastic approximation? As its name suggests, it is a method that uses data to approximate (typically) expectations. For example, suppose
For a Google term you could use "computational creativity". It covers a wide range of ideas, and the artist here is not using one single tool or approach.
There are clearly a range of different techniques that went into the artist's installations that were shown on the video. I have an idea about a few of them:
Some are from basic animation and ...
The paper Comparison between genetic algorithms and particle swarm optimization (1998, by Eberhart and Shi) does not really answer the question of when to use one over the other (this may be an open question), but at least it provides a comparison of how the methods work and what could affect their performance (i.e. which parameters or operators they use, ...
The DEAP library (a Python library for EAs) contains some benchmarks. In particular, you may want to look at the following functions
dtlz1, dtlz2, dtlz3, dtlz4, dtlz5, dtlz6, dtlz7
Besides the DEAP library repository, you might want to look at several well ...
In general, NO.
You don't get the "true" state value function. TD-learning approximates the true value function. It can be a very close, or even exact approximation in simple cases, but, in general, it is just an approximation.
Depending on the difficulty of the problem, a non-constant $\alpha$ value can help the policy approximate the true value ...
In computer vision, the problem of filling missing parts of an image is called image inpainting; the subtask of filling the surroundings is called image outpainting in , which is your problem.
The methods for solving the image outpainting problem are not mature according to the pre-print paper Image Outpainting and Harmonization using Generative ...