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Q-Learning

122 papers with code · Methodology

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

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Greatest papers with code

Revisiting Fundamentals of Experience Replay

ICML 2020 google-research/google-research

Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding.

DQN REPLAY DATASET Q-LEARNING

Deep Reinforcement Learning with Double Q-learning

22 Sep 2015tensorpack/tensorpack

The popular Q-learning algorithm is known to overestimate action values under certain conditions.

ATARI GAMES Q-LEARNING

Playing Atari with Deep Reinforcement Learning

19 Dec 2013tensorpack/tensorpack

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.

ATARI GAMES Q-LEARNING

Increasing the Action Gap: New Operators for Reinforcement Learning

15 Dec 2015janhuenermann/neurojs

Extending the idea of a locally consistent operator, we then derive sufficient conditions for an operator to preserve optimality, leading to a family of operators which includes our consistent Bellman operator.

ATARI GAMES Q-LEARNING

Addressing Function Approximation Error in Actor-Critic Methods

ICML 2018 facebookresearch/ReAgent

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.

OPENAI GYM Q-LEARNING

Continuous control with deep reinforcement learning

9 Sep 2015facebookresearch/Horizon

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.

CONTINUOUS CONTROL Q-LEARNING

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

6 May 2016NervanaSystems/coach

Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world.

ATARI GAMES GAME OF DOOM Q-LEARNING

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

3 Sep 2019astooke/rlpyt

rlpyt is designed as a high-throughput code base for small- to medium-scale research in deep RL.

Q-LEARNING

Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

NeurIPS 2018 uber-common/deep-neuroevolution

Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e. g. hours vs. days) because they parallelize better.

POLICY GRADIENT METHODS Q-LEARNING