Every year there is a brand new reinforcement learning competition. This usually consists of new organizers, and a new website! Instead of replacing the old website every year and breaking hundreds of links, we use a different subdomain each year. So, this page will always exist at: http://rl-competition.org And the specific websites for different years are: NIPS Reinforcement Learning Workshop: Benchmarks and Bakeoffs NIPS Reinforcement Learning Workshop: Benchmarks and Bakeoffs II ICML Reinforcement Learning and Benchmarking Event NIPS Workshop: The First Annual Reinforcement Learning Competition The 2008 Reinforcement Learning Competition:: http://2008.rl-competition.org
Learn to apply Reinforcement Learning and Artificial Intelligence algorithms using Python, Pytorch and OpenAI Gym Artificial Intelligence is dynamically edging its way into our lives. It is already broadly available and we use it - sometimes even not knowing it - on daily basis. Soon it will be our permanent, every day companion. And where can we place Reinforcement Learning in AI world? Definitely this is one of the most promising and fastest growing technologies that can eventually lead us to General Artificial Intelligence!
In this era of automation, we hear so often about autonomous cars or robots outperforming people or, a computer game defeating the best of the champions! And yes, these are some of the most beautiful and indeed complex applications of Deep Reinforcement Learning. The aim of this article is to present the concept of reinforcement learning. The literal meaning of the word'reinforce' is'to strengthen'. Reinforcement in psychology is to establish or encourage a pattern of behavior by providing a stimulus that can increase the probability of the desired behavior.
Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this article, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today. At the core of reinforcement learning is the concept that the optimal behavior or action is reinforced by a positive reward. Similar to toddlers learning how to walk who adjust actions based on the outcomes they experience such as taking a smaller step if the previous broad step made them fall, machines and software agents use reinforcement learning algorithms to determine the ideal behavior based upon feedback from the environment. Depending on the complexity of the problem, reinforcement learning algorithms can keep adapting to the environment over time if necessary in order to maximize the reward in the long-term.