Q-Learning
390 papers with code • 0 benchmarks • 2 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Q-Learning
Libraries
Use these libraries to find Q-Learning models and implementations
VizDoom
Yeast
Most implemented papers
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
corl-team/CORL
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NeurIPS 2021
However, prior methods typically require accurate estimation of the behavior policy or sampling from OOD data points, which themselves can be a non-trivial problem.
Deep Reinforcement Learning with a Natural Language Action Space
This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games.
Taming the Noise in Reinforcement Learning via Soft Updates
We propose G-learning, a new off-policy learning algorithm that regularizes the value estimates by penalizing deterministic policies in the beginning of the learning process.
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
iassael/learning-to-communicate
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NeurIPS 2016
We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility.
Reinforcement Learning with Deep Energy-Based Policies
We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before.
Mean Field Multi-Agent Reinforcement Learning
Existing multi-agent reinforcement learning methods are limited typically to a small number of agents.
Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks
Ring367/A-Deep-Reinforcement-Learning-Network-for-Traffic-Light-Cycle-Control
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In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data.
Deep Quality-Value (DQV) Learning
paintception/Deep-Quality-Value-DQV-Learning-
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We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning.
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
ChunyuanLI/RAS
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We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples.
Deep Reinforcement Learning for Imbalanced Classification
linenus/DRL-For-imbalanced-Classification
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The agent finally finds an optimal classification policy in imbalanced data under the guidance of specific reward function and beneficial learning environment.
