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 8976–9000 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Function Properties in Mean Reversion StrategiesCode0
Safe Coupled Deep Q-Learning for Recommendation Systems—0
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
Evolving Reinforcement Learning AlgorithmsCode1
Simulating SQL Injection Vulnerability Exploitation Using Q-Learning Reinforcement Learning AgentsCode1
An Adaptive Multi-Agent Physical Layer Security Framework for Cognitive Cyber-Physical Systems—0
CoachNet: An Adversarial Sampling Approach for Reinforcement Learning—0
Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement LearningCode1
Coding for Distributed Multi-Agent Reinforcement Learning—0
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach—0
qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems—0
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from PixelsCode1
Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces—0
Reinforcement Learning with Latent FlowCode1
Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint—0
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints—0
Geometric Entropic Exploration—0
Deep Reinforcement Learning with Quantum-inspired Experience Replay—0
Enhanced Audit Techniques Empowered by the Reinforcement Learning Pertaining to IFRS 16 Lease—0
An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation—0
Reinforcement Learning based Collective Entity Alignment with Adaptive FeaturesCode0
Markov Chain Monte Carlo Policy Optimization—0
MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal ControlCode1
A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion RecognitionCode0
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
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Benchmark Results

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