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 3701–3725 of 15113 papers

TitleStatusHype
Optimality theory of stigmergic collective information processing by chemotactic cells—0
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning—0
Phase Re-service in Reinforcement Learning Traffic Signal Control—0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
Track-MDP: Reinforcement Learning for Target Tracking with Controlled Sensing—0
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer—0
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving GeneralizationCode0
Random Latent Exploration for Deep Reinforcement Learning—0
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction—0
Reinforcement Learning: Tutorial and Survey—0
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender SystemsCode0
Sparsity-based Safety Conservatism for Constrained Offline Reinforcement Learning—0
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
Balancing the Scales: Reinforcement Learning for Fair ClassificationCode0
Deflated Dynamics Value Iteration—0
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control AgentsCode0
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation—0
Affordance-Guided Reinforcement Learning via Visual Prompting—0
Learning to Steer Markovian Agents under Model UncertaintyCode0
Deep reinforcement learning with symmetric data augmentation applied for aircraft lateral attitude tracking control—0
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods—0
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control—0
PID Accelerated Temporal Difference Algorithms—0
Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach—0
A Review of Nine Physics Engines for Reinforcement Learning Research—0
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Benchmark Results

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