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 9651–9675 of 15113 papers

TitleStatusHype
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach—0
Geometric Entropic Exploration—0
Deep Reinforcement Learning with Quantum-inspired Experience Replay—0
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints—0
Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint—0
Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces—0
Reinforcement Learning based Collective Entity Alignment with Adaptive FeaturesCode0
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
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
Markov Chain Monte Carlo Policy Optimization—0
Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning—0
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments—0
Reinforcement Learning for Flexibility Design Problems—0
PERIL: Probabilistic Embeddings for hybrid Meta-Reinforcement and Imitation Learning—0
Sample efficient Quality Diversity for neural continuous control—0
Playing Atari with Capsule Networks: A systematic comparison of CNN and CapsNets-based agents.—0
Robust Imitation via Decision-Time Planning—0
Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples—0
Representation Balancing Offline Model-based Reinforcement Learning—0
Unsupervised Active Pre-Training for Reinforcement Learning—0
Robust Offline Reinforcement Learning from Low-Quality Data—0
Reinforcement Learning for Control with Probabilistic Stability Guarantee—0
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Benchmark Results

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