Well, point 1 and point 2 are hard to simultaneously satisfy, since doing reinforcement learning at scale is pretty much a research topic on its own. Reasoning: the number of decisions it has to make during a game is small, so reinforcement learning should work pretty well. There's a couple of levels of refinement you could try to extend the project - basic model is'given game state, how do I bid?'; first extension is'given game sequence, how do I bid?'; second extension is'given a sequence of previous games with these particular players, how do I bid?'.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Summary: At the core of modern AI, particularly robotics, and sequential tasks is Reinforcement Learning. Although RL has been around for many years it has become the third leg of the Machine Learning stool and increasingly important for Data Scientist to know when and how to implement. If you poled a group of data scientist just a few years back about how many machine learning problem types there are you would almost certainly have gotten a binary response: problem types were clearly divided into supervised and unsupervised. While Reinforcement Learning (RL) has been around since at least the 80's and before that in the behavioral sciences, its introduction as a major player in machine learning reflects it rising importance in AI. What problems fit this description?

I wrote this post earlier this year but never came around to hitting the publish button. I hope it can be useful as an intro to self reinforcement learning and combining that with neural networks. I'm also using another dataset than the typical toy grid worlds, which hopefully is refreshing:-)

I am self-learning Reinforcement-Learning material, mainly using https://sites.ualberta.ca/ The environment I am testing the algorithms in is pretty simple: 3 predators agents, 1 randomly moving prey, grid world (about 15x15) and they can move up,down,left,right. At the moment I am learning about function approximation.The update quantity I am using is learning_rate * bellman_error * gradient Q(X_t, A_t) as seen on page 59 of the above paper. Equally if I use a linear function or a neural network, my weights diverge very quickly (using SARSA, I didn't try Q-Learning yet but I would be suprised if they wouldn't diverge there). I checked the calculations the algorithm makes by hand and it seems right.

It's a problem we can all relate to: sitting in traffic and waiting for a green light. While waiting, you may have even pondered how you would try to improve traffic efficiency--surely there's got to be some way for everyone to get to work on time. But ponder no longer, because a team of engineers from Tsinghua University in China has handed the problem over to machines. The team's recent study makes use of deep reinforcement learning algorithms to optimize traffic signaling, and its promising results suggest there may be a way to arrive on time after all. Let's be clear: traffic is a complex problem to solve, and traffic control engineers have long worked on improving efficiency.

Are there any active communities (forums, Q&A) for reinforcement learning? For machinelearning in general there is this subreddit and the freenode irc channel for example. But their communities seem both to be mainly focused on deep learning. There is no freenode RL channel and the subreddit is quite dead. I am self-learning RL and often have questions (practical and theoretical ones) and am not quite sure where to ask or even where to get relevant news and an overview of the state of that field for.

In this paper we study a model-based approach to calculating approximately optimal policies in Markovian Decision Processes. In particular, we derive novel bounds on the loss of using a policy derived from a factored linear model, a class of models which generalize numerous previous models out of those that come with strong computational guarantees. For the first time in the literature, we derive performance bounds for model-based techniques where the model inaccuracy is measured in weighted norms. Moreover, our bounds show a decreased sensitivity to the discount factor and, unlike similar bounds derived for other approaches, they are insensitive to measure mismatch. Similarly to previous works, our proofs are also based on contraction arguments, but with the main differences that we use carefully constructed norms building on Banach lattices, and the contraction property is only assumed for operators acting on "compressed" spaces, thus weakening previous assumptions, while strengthening previous results.

I feel like supplying the target image as a reference kinda defeats the purpose and makes the task significantly easier. The target image should be something more semantic like a canonical microwave, not an image of the actual microwave which exists in the scene.

Advances in machine learning have been driven by innovations and ideas from many fields. Inspired by the way that humans learn, Reinforcement Learning (RL) is concerned with algorithms which improve with trial-and-error feedback to optimize future performance. Board games and video games often have well-defined reward functions which allow for straightforward optimization with RL algorithms. Algorithmic advances have allowed for RL to be in real-world problems, such as high degree-of-freedom robotic manipulation and large-scale recommendation tasks, with more complex goals. Twitter Cortex invests in novel state-of-the-art machine learning methods to improve the quality of our products.