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Various techniques exist to train policies to solve tasks with deep reinforcement learning algorithms, each having their own benefits. At the highest level, there is a distinction between model-based and model-free reinforcement learning, which refers to whether the algorithm attempts to learn a forward model of the environment dynamics.
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
The developers have not publicly released the code or architecture of their model, but have listed several state of the art machine learning techniques such as relational deep reinforcement learning, long short-term memory, auto-regressive policy heads, pointer networks, and centralized value baseline. [4]
He studied at Christ's College, Cambridge, [3] graduating in 1997 with the Addison-Wesley award, and having befriended Demis Hassabis whilst at Cambridge. [4] Silver returned to academia in 2004 at the University of Alberta to study for a PhD on reinforcement learning, [5] where he co-introduced the algorithms used in the first master-level 9×9 Go programs and graduated in 2009.
General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully. [1] [2] [3] For many games like chess, computers are programmed to play these games using a specially designed algorithm, which cannot be transferred to another context.
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent's decision function to accomplish difficult tasks. PPO was developed by John Schulman in 2017, [1] and had become the default RL algorithm at the US artificial intelligence company OpenAI. [2]
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. Pages in category "Reinforcement learning"
Continuous Deep Q-Learning with Model-based Acceleration. arXiv:1603.00748 [5] [circular reference] Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine (2016). Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates. arXiv:1610.00633