SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 8901–8925 of 15113 papers

TitleStatusHype
Universal Trading for Order Execution with Oracle Policy Distillation—0
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
Exploring the Impact of Tunable Agents in Sequential Social DilemmasCode0
Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems—0
Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks—0
Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning—0
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors—0
Data sharing gamesCode0
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach—0
ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning—0
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task—0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers—0
GST: Group-Sparse Training for Accelerating Deep Reinforcement Learning—0
Learning Synthetic Environments for Reinforcement Learning with Evolution StrategiesCode1
Solving optimal stopping problems with Deep Q-Learning—0
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art—0
Decoupled Exploration and Exploitation Policies for Sample-Efficient Reinforcement Learning—0
BF++: a language for general-purpose program synthesisCode0
Feature Selection Using Reinforcement Learning—0
Theory of Mind for Deep Reinforcement Learning in HanabiCode0
Prior Preference Learning from Experts:Designing a Reward with Active Inference—0
Differentiable Trust Region Layers for Deep Reinforcement LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified