Feb 25, 2015 ， Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn ...
Human-Level Control Through Deep Reinforcement Learning ... This is a pytorch implementation of Deep Q Networks as described in the paper above. Requirements + ...
This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to ...
This repository implements the notable paper: Human-level control through deep reinforcement learning. This paper is widely known for a famous video clip, ...
Feb 26, 2015 ， Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn ...
Feb 26, 2015 ， This research is cool work, but by no means revolutionary. As Jurgen says, much of the work had already been completed and published by a few of the coauthors.
Feb 25, 2015 ， This work offers the first demonstration of a general purpose learning agent that can be trained end-to-end to handle a wide variety of challenging tasks.
To train an agent that interacts (performs actions) with the environment given the observation such that it will receive the maximum accumulated reward at the ...
Deep reinforcement learning algorithm has been applied widely in computer games, automatic driving, natural language processing, recommendation system and robot ...
Former reinforcement learning agents are successful in some domains in which useful features can be handcrafted, or in fully observed, low dimensional state ...
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