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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8901–8950 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
Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing—0
Robust Reinforcement Learning on State Observations with Learned Optimal AdversaryCode1
Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis TreatmentCode1
Model-based Policy Search for Partially Measurable Systems—0
mt5se: An Open Source Framework for Building Autonomous Trading RobotsCode1
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with TransformersCode1
Flocking and Collision Avoidance for a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning—0
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination—0
Deep Reinforcement Learning for Producing Furniture Layout in Indoor ScenesCode1
Grounding Language to Entities and Dynamics for Generalization in Reinforcement LearningCode1
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning—0
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search—0
Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments—0
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning ApproachCode1
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
Deep Reinforcement Learning with Embedded LQR Controllers—0
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition—0
Deep Reinforcement Learning for Active High Frequency TradingCode1
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees—0
Stable deep reinforcement learning method by predicting uncertainty in rewards as a subtask—0
Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications—0
Regularized Policies are Reward Robust—0
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning—0
Hierarchical Reinforcement Learning By Discovering Intrinsic OptionsCode1
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Benchmark Results

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