In addition to exercises and solution, each folder also contains a list of learning goals, a brief concept summary, and links to the relevant readings. All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.

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

Reinforcement learning is one of the subfields of machine learning. The machine learning model can gain abilities to make decisions and explore in an unsupervised and complex environment by reinforcement learning. Reinforcement learning models use rewards for their actions to reach their goal/mission/task for what they are used to. So, reinforcement learning is different from supervised and unsupervised learning models. Reward rules are determined in the reinforcement learning algorithms.

Rich S. Sutton, a research scientist at DeepMind and computing science professor at the University of Alberta, explains the underlying formal problem like the Markov decision processes, core solution methods, dynamic programming, Monte Carlo methods, and temporal-difference learning in this in-depth tutorial.

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!