We present the first deep learning model to successfully learn control policies di-rectly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learn-ing Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
International audienceConsistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is...
Reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a s...
International audienceConsistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is...
Games for the Atari 2600 console provide great environments for testing reinforcement learning algor...
Games for the Atari 2600 console provide great environments for testing reinforcement learning algor...
We present an implementation of a specific type of deep reinforcement learning algorithm known as de...
We present an implementation of a specific type of deep reinforcement learning algorithm known as de...
Reinforcement learning is concerned with learning to interact with environments that are initially u...
Recently it has been shown that deep neural networks can learn to play Atari games by directly obser...
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has ...
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has ...
This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor...
The combination of modern Reinforcement Learning and Deep Learning ap-proaches holds the promise of ...
The combination of modern Reinforcement Learning and Deep Learning ap-proaches holds the promise of ...
The combination of modern Reinforcement Learning and Deep Learning ap-proaches holds the promise of ...
International audienceConsistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is...
Reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a s...
International audienceConsistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is...
Games for the Atari 2600 console provide great environments for testing reinforcement learning algor...
Games for the Atari 2600 console provide great environments for testing reinforcement learning algor...
We present an implementation of a specific type of deep reinforcement learning algorithm known as de...
We present an implementation of a specific type of deep reinforcement learning algorithm known as de...
Reinforcement learning is concerned with learning to interact with environments that are initially u...
Recently it has been shown that deep neural networks can learn to play Atari games by directly obser...
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has ...
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has ...
This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor...
The combination of modern Reinforcement Learning and Deep Learning ap-proaches holds the promise of ...
The combination of modern Reinforcement Learning and Deep Learning ap-proaches holds the promise of ...
The combination of modern Reinforcement Learning and Deep Learning ap-proaches holds the promise of ...
International audienceConsistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is...
Reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a s...
International audienceConsistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is...