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 2651–2675 of 15113 papers

TitleStatusHype
The Effective Horizon Explains Deep RL Performance in Stochastic EnvironmentsCode1
An Invitation to Deep Reinforcement Learning—0
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation—0
Toward a Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency—0
A dynamical clipping approach with task feedback for Proximal Policy OptimizationCode0
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL ApproachCode1
Sequential Planning in Large Partially Observable Environments guided by LLMsCode1
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms—0
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing—0
Reward Certification for Policy Smoothed Reinforcement LearningCode0
Spreeze: High-Throughput Parallel Reinforcement Learning Framework—0
KnowGPT: Knowledge Graph based Prompting for Large Language Models—0
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and RegularizationCode0
The Generalization Gap in Offline Reinforcement LearningCode1
Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Tasks—0
On the calibration of compartmental epidemiological modelsCode0
Evolving Reservoirs for Meta Reinforcement LearningCode2
PerfRL: A Small Language Model Framework for Efficient Code Optimization—0
Guaranteed Trust Region Optimization via Two-Phase KL Penalization—0
UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal ControlCode1
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy LabelsCode0
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
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Benchmark Results

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