Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, reinforcement learning is called approximate dynamic programming, or neuro-dynamic programming. The problems of interest in reinforcement learning have also been studied in the theory of optimal control, which is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact computation, and less with learning or approximation, particularly in the absence of a mathematical model of the environment. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality. In machine learning, the environment is typically formulated as a Markov Decision Process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.

Curiosity and Procrastination in Reinforcement Learning

Posted by Nikolay Savinov, Research Intern, Google Brain Team and Timothy Lillicrap, Research Scientist, DeepMind Reinforcement learning (RL) is one of the most actively pursued...

Open sourcing TRFL: a library of reinforcement learning building blocks

Today we are open sourcing a new library of useful building blocks for writing reinforcement learning (RL) agents in TensorFlow. Named TRFL (pronounced ‘truffle’), it represents a collection of key algorithmic components that we have used internally for a large number of our most successful agents such as DQN, DDPG and the Importance Weighted Actor [...]

OpenAI launches Spinning Up, a learning resource for potential deep learning...

OpenAI released Spinning Up yesterday. It is an educational resource for anyone who wants to become a skilled deep learning practitioner. Spinning Up has many examples in reinforcement learning, documentation, and tutorials. The inspiration to build Spinning Up comes from OpenAI Scholars and Fellows initiatives. They observed that it’s possible for people with little-to-no experience [...]

DeepMind open sources TRFL, a new library of reinforcement learning building...

The DeepMind team announced yesterday that they’re open sourcing a new library, named TRFL, that comprises useful building blocks for writing reinforcement learning (RL) agents in TensorFlow. The TRFL library was created by the research engineering team at DeepMind. TRFL library is a collection of key algorithmic components that are used for a large number [...]