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 12651–12700 of 15113 papers

TitleStatusHype
Inverse Reinforcement Learning in Contextual MDPsCode0
From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving—0
Unknown mixing times in apprenticeship and reinforcement learning—0
Scene Induced Multi-Modal Trajectory Forecasting via Planning—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
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
Perceptual Values from Observation—0
Reinforcement Learning without Ground-Truth State—0
Stochastic Variance Reduction for Deep Q-learning—0
A Bayesian Approach to Robust Reinforcement Learning—0
Deep Reinforcement Learning Based Parameter Control in Differential EvolutionCode0
Issues concerning realizability of Blackwell optimal policies in reinforcement learning—0
Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorial—0
Evolving Rewards to Automate Reinforcement Learning—0
A Regularized Opponent Model with Maximum Entropy ObjectiveCode0
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks—0
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning—0
Exact-K Recommendation via Maximal Clique OptimizationCode0
In Support of Over-Parametrization in Deep Reinforcement Learning: an Empirical Study—0
Mastering the Game of Sungka from Random PlayCode0
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning—0
Stochastically Dominant Distributional Reinforcement Learning—0
Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem—0
TBQ(σ): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning—0
Meta Reinforcement Learning with Task Embedding and Shared PolicyCode0
Meta-Reinforcement Learning for Adaptive Autonomous Driving—0
Random Expert Distillation: Imitation Learning via Expert Policy Support EstimationCode0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature SelectionCode0
Sub-policy Adaptation for Hierarchical Reinforcement Learning—0
Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning—0
Knowledge-Based Sequential Decision-Making Under Uncertainty—0
Goal-conditioned Imitation Learning—0
Leveraging exploration in off-policy algorithms via normalizing flowsCode0
Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds—0
Autonomous Penetration Testing using Reinforcement Learning—0
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment—0
Deep reinforcement learning for scheduling in large-scale networked control systems—0
Deep Reinforcement Learning for Scheduling in Cellular Networks—0
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic FunctionsCode0
A Learning based Branch and Bound for Maximum Common Subgraph Problems—0
Meta reinforcement learning as task inferenceCode0
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction—0
Variational Regret Bounds for Reinforcement Learning—0
Trajectory-Based Off-Policy Deep Reinforcement LearningCode0
TauRieL: Targeting Traveling Salesman Problem with a deep reinforcement learning inspired architecture—0
Successor Options: An Option Discovery Framework for Reinforcement LearningCode0
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Benchmark Results

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