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 11001–11025 of 15113 papers

TitleStatusHype
From Persistent Homology to Reinforcement Learning with Applications for Retail Banking—0
From Pixels to Torques: Policy Learning with Deep Dynamical Models—0
From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions—0
From self-tuning regulators to reinforcement learning and back again—0
From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving—0
From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning—0
From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation—0
From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning—0
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement Learning Using Unique Experiences—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Full Gradient DQN Reinforcement Learning: A Provably Convergent Scheme—0
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning—0
Fully Convolutional Attention Networks for Fine-Grained Recognition—0
Fully Decentralized Model-based Policy Optimization with Networked Agents—0
Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power—0
Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning—0
Functional Optimization Reinforcement Learning for Real-Time Bidding—0
Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -—0
Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision—0
Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer—0
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Benchmark Results

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