Human-level control through deep reinforcement learning
The theory of reinforcement learning provides a normative account 1, deeply rooted in
psychological 2 and neuroscientific 3 perspectives on animal behaviour, of how agents may
optimize their control of an environment. To use reinforcement learning successfully in ...
psychological 2 and neuroscientific 3 perspectives on animal behaviour, of how agents may
optimize their control of an environment. To use reinforcement learning successfully in ...
The arcade learning environment: An evaluation platform for general agents
MG Bellemare, Y Naddaf, J Veness… - Journal of Artificial …, 2012 - arxiv.org
Abstract: In this article we introduce the Arcade Learning Environment (ALE): both a
challenge problem and a platform and methodology for evaluating the development of
general, domain-independent AI technology. ALE provides an interface to hundreds of ...
challenge problem and a platform and methodology for evaluating the development of
general, domain-independent AI technology. ALE provides an interface to hundreds of ...
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Playing atari with deep reinforcement learning
Abstract: We present the first deep learning model to successfully learn control policies
directly 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 ...
directly 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 ...
Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning
Abstract The combination of modern Reinforcement Learning and Deep Learning
approaches holds the promise of making significant progress on challenging applications
requiring both rich perception and policy-selection. The Arcade Learning Environment ( ...
approaches holds the promise of making significant progress on challenging applications
requiring both rich perception and policy-selection. The Arcade Learning Environment ( ...
[PDF][PDF] End-to-end training of deep visuomotor policies
Abstract Policy search methods can allow robots to learn control policies for a wide range of
tasks, but practical applications of policy search often require hand-engineered components
for perception, state estimation, and low-level control. In this paper, we aim to answer the ...
tasks, but practical applications of policy search often require hand-engineered components
for perception, state estimation, and low-level control. In this paper, we aim to answer the ...
Cited by 114 Related articles All 10 versions Cite SaveSaving...Error saving. Try again? More EBSCOhost Full Text View as HTML Fewer
Mastering the game of Go with deep neural networks and tree search
The game of Go has long been viewed as the most challenging of classic games for artificial
intelligence owing to its enormous search space and the difficulty of evaluating board
positions and moves. Here we introduce a new approach to computer Go that uses 'value ...
intelligence owing to its enormous search space and the difficulty of evaluating board
positions and moves. Here we introduce a new approach to computer Go that uses 'value ...
Cited by 310 Related articles All 32 versions Cite SaveSaving...Error saving. Try again? More EBSCOhost Full Text Fewer
[PDF][PDF] Deep reinforcement learning with double Q-learning
Abstract The popular Q-learning algorithm is known to overestimate action values under
certain conditions. It was not previously known whether, in practice, such overestimations
are common, whether they harm performance, and whether they can generally be ...
certain conditions. It was not previously known whether, in practice, such overestimations
are common, whether they harm performance, and whether they can generally be ...
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Continuous control with deep reinforcement learning
Abstract: We adapt the ideas underlying the success of Deep Q-Learning to the continuous
action domain. We present an actor-critic, model-free algorithm based on the deterministic
policy gradient that can operate over continuous action spaces. Using the same learning ...
action domain. We present an actor-critic, model-free algorithm based on the deterministic
policy gradient that can operate over continuous action spaces. Using the same learning ...
Prioritized experience replay
Abstract: Experience replay lets online reinforcement learning agents remember and reuse
experiences from the past. In prior work, experience transitions were uniformly sampled from
a replay memory. However, this approach simply replays transitions at the same ...
experiences from the past. In prior work, experience transitions were uniformly sampled from
a replay memory. However, this approach simply replays transitions at the same ...
Deep learning
Deep learning allows computational models that are composed of multiple processing
layers to learn representations of data with multiple levels of abstraction. These methods
have dramatically improved the state-of-the-art in speech recognition, visual object ...
layers to learn representations of data with multiple levels of abstraction. These methods
have dramatically improved the state-of-the-art in speech recognition, visual object ...
Cited by 1016 Related articles All 12 versions Cite SaveSaving...Error saving. Try again? More Fewer