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 3751–3775 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning for Power System Control: A Review—0
Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning—0
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators—0
A Review of Safe Reinforcement Learning Methods for Modern Power Systems—0
Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes—0
Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks—0
Operator World Models for Reinforcement LearningCode0
Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning ProgramsCode0
External Model Motivated Agents: Reinforcement Learning for Enhanced Environment SamplingCode0
Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems—0
Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels—0
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control PriorsCode0
Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learning—0
Beyond Human Preferences: Exploring Reinforcement Learning Trajectory Evaluation and Improvement through LLMs—0
Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planning—0
Multi-agent Cooperative Games Using Belief Map Assisted TrainingCode0
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion—0
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control—0
Combining Automated Optimisation of Hyperparameters and Reward ShapeCode0
Preference Elicitation for Offline Reinforcement Learning—0
Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations—0
Human-Object Interaction from Human-Level Instructions—0
The State-Action-Reward-State-Action Algorithm in Spatial Prisoner's Dilemma Game—0
ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI FeedbackCode0
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Benchmark Results

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