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 12001–12050 of 15113 papers

TitleStatusHype
Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection—0
Runtime Safety Assurance Using Reinforcement Learning—0
Runtime Verification of Learning Properties for Reinforcement Learning Algorithms—0
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
S2VG: Soft Stochastic Value Gradient method—0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning—0
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics—0
SAC-GLAM: Improving Online RL for LLM agents with Soft Actor-Critic and Hindsight Relabeling—0
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons—0
Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking—0
Safe and Robust Reinforcement Learning: Principles and Practice—0
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation—0
Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics—0
Safe Control and Learning Using the Generalized Action Governor—0
Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning—0
Debiased Off-Policy Evaluation for Recommendation Systems—0
Safe Coupled Deep Q-Learning for Recommendation Systems—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning—0
Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks—0
Safe Deep Reinforcement Learning by Verifying Task-Level Properties—0
Safe Distributional Reinforcement Learning—0
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation—0
Safe Evaluation For Offline Learning: Are We Ready To Deploy?—0
Safe Exploration by Solving Early Terminated MDP—0
Safe Exploration for Identifying Linear Systems via Robust Optimization—0
Safe Exploration in Linear Equality Constraint—0
Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions—0
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems—0
Safe Exploration of State and Action Spaces in Reinforcement Learning—0
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Safe Inverse Reinforcement Learning via Control Barrier Function—0
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art—0
Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units—0
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Safe Planning and Policy Optimization via World Model Learning—0
Safe Policies for Reinforcement Learning via Primal-Dual Methods—0
Safe Policy Improvement for POMDPs via Finite-State Controllers—0
Safe Policy Improvement in Constrained Markov Decision Processes—0
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret—0
Safe RAN control: A Symbolic Reinforcement Learning Approach—0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition—0
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition—0
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman—0
SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning—0
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Benchmark Results

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