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 9651–9700 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
Prior Preference Learning From Experts: Designing A Reward with Active Inference—0
Unbiased learning with State-Conditioned Rewards in Adversarial Imitation Learning—0
Optimizing Information Bottleneck in Reinforcement Learning: A Stein Variational Approach—0
PODS: Policy Optimization via Differentiable Simulation—0
Plan-Based Asymptotically Equivalent Reward Shaping—0
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization—0
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic—0
Trust, but verify: model-based exploration in sparse reward environmentsCode0
Monte-Carlo Planning and Learning with Language Action Value Estimates—0
Meta-Reinforcement Learning With Informed Policy Regularization—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
Reinforcement Learning Based Asymmetrical DNN Modularization for Optimal Loading—0
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous ControlCode0
Practical Marginalized Importance Sampling with the Successor Representation—0
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
Success-Rate Targeted Reinforcement Learning by Disorientation Penalty—0
ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward—0
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study—0
The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning—0
Uncertainty Weighted Offline Reinforcement Learning—0
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning—0
Optimistic Policy Optimization with General Function Approximations—0
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Offline Policy Optimization with Variance Regularization—0
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Benchmark Results

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