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 12501–12550 of 15113 papers

TitleStatusHype
Continual Reinforcement Learning in 3D Non-stationary EnvironmentsCode0
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar—0
Scene Induced Multi-Modal Trajectory Forecasting via Planning—0
PAC Guarantees for Cooperative Multi-Agent Reinforcement Learning with Restricted Communication—0
Recurrent Value Functions—0
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs—0
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document TraversalCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving—0
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable PoliciesCode0
Unknown mixing times in apprenticeship and reinforcement learning—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Inverse Reinforcement Learning in Contextual MDPsCode0
Hierarchical Reinforcement Learning for Quadruped Locomotion—0
Deep Reinforcement Learning for Detecting Malicious Websites—0
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven ExplorationCode0
Stochastic Inverse Reinforcement Learning—0
Maximum Entropy-Regularized Multi-Goal Reinforcement LearningCode1
Issues concerning realizability of Blackwell optimal policies in reinforcement learning—0
Deep Reinforcement Learning Based Parameter Control in Differential EvolutionCode0
A Bayesian Approach to Robust Reinforcement Learning—0
Stochastic Variance Reduction for Deep Q-learning—0
Reinforcement Learning without Ground-Truth State—0
Perceptual Values from Observation—0
Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorial—0
Evolving Rewards to Automate Reinforcement Learning—0
In Support of Over-Parametrization in Deep Reinforcement Learning: an Empirical Study—0
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning—0
A Regularized Opponent Model with Maximum Entropy ObjectiveCode0
Exact-K Recommendation via Maximal Clique OptimizationCode0
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks—0
Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem—0
TBQ(σ): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning—0
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning—0
Stochastically Dominant Distributional Reinforcement Learning—0
Mastering the Game of Sungka from Random PlayCode0
Meta-Reinforcement Learning for Adaptive Autonomous Driving—0
Sub-policy Adaptation for Hierarchical Reinforcement Learning—0
Goal-conditioned Imitation Learning—0
Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning—0
Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds—0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature SelectionCode0
Meta Reinforcement Learning with Task Embedding and Shared PolicyCode0
Random Expert Distillation: Imitation Learning via Expert Policy Support EstimationCode0
Leveraging exploration in off-policy algorithms via normalizing flowsCode0
Knowledge-Based Sequential Decision-Making Under Uncertainty—0
A Learning based Branch and Bound for Maximum Common Subgraph Problems—0
Autonomous Penetration Testing using Reinforcement Learning—0
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment—0
Deep Reinforcement Learning for Scheduling in Cellular Networks—0
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Benchmark Results

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