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.

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.

RLAI research program pursues an approach to artificial-intelligence and engineering problems in which they are formulated as large optimal-control problems and approximately solved using reinforcement-learning methods. Reinforcement learning is a new body of theory and techniques for optimal control that has been developed in the last twenty years primarily within the machine learning and operations research communities, and which have separately become important in psychology and neuroscience. Reinforcement learning researchers have developed novel methods to approximate solutions to optimal-control problems that are too large or too ill-defined for classical solution methods such as dynamic programming. For example, reinforcement-learning methods have obtained the best known solutions in such diverse automation applications as helicopter flying, elevator scheduling, playing backgammon, and resource-constrained scheduling. The objectives of the RLAI research program are to create new methods for reinforcement learning that remove some of the limitations on its widespread application and to develop reinforcement learning as a model of intelligence that could approach human abilities.

Google DeepMind implemented an artificial intelligence program using deep reinforcement learning that plays Atari games and improves itself to a superhuman level. The technique is called deep Q-learning, it uses a combination of deep neural networks and reinforcement learning, and it is capable of playing many Atari games as good or better than humans. After presenting their initial results with the algorithm, Google almost immediately acquired the company for several hundred million dollars, hence the name Google DeepMind. The thumbnail was made by moparx - https://flic.kr/p/76foMV