Deep Reinforcement Learning
1654 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find Deep Reinforcement Learning models and implementationsMost implemented papers
Continuous control with deep reinforcement learning
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.
Playing Atari with Deep Reinforcement Learning
labmlai/annotated_deep_learning_paper_implementations
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We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
Deep Reinforcement Learning with Double Q-learning
labmlai/annotated_deep_learning_paper_implementations
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The popular Q-learning algorithm is known to overestimate action values under certain conditions.
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
We explore deep reinforcement learning methods for multi-agent domains.
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
A platform for Applied Reinforcement Learning (Applied RL)
Dueling Network Architectures for Deep Reinforcement Learning
labmlai/annotated_deep_learning_paper_implementations
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In recent years there have been many successes of using deep representations in reinforcement learning.
Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.
Rainbow: Combining Improvements in Deep Reinforcement Learning
thu-ml/tianshou
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The deep reinforcement learning community has made several independent improvements to the DQN algorithm.
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.
