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 1576–1600 of 15113 papers

TitleStatusHype
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL—0
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning—0
The Central Role of the Loss Function in Reinforcement Learning—0
Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition—0
An Enhanced-State Reinforcement Learning Algorithm for Multi-Task Fusion in Large-Scale Recommender Systems—0
On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration—0
A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler—0
Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic SystemsCode1
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers—0
Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers—0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
Instigating Cooperation among LLM Agents Using Adaptive Information Modulation—0
Enhancing RL Safety with Counterfactual LLM ReasoningCode1
Robust Reinforcement Learning with Dynamic Distortion Risk MeasuresCode0
Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies—0
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning—0
Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema—0
KAN v.s. MLP for Offline Reinforcement Learning—0
PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion—0
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
Quasimetric Value Functions with Dense Rewards—0
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Benchmark Results

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