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

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Showing 5626–5650 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning in Tensor Reproducing Kernel Hilbert Space—0
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction—0
Safe Reinforcement Learning on Autonomous Vehicles—0
Safe Reinforcement Learning through Meta-learned Instincts—0
Safe Reinforcement Learning using Data-Driven Predictive Control—0
Safe Reinforcement Learning Using Robust Action Governor—0
Safe Reinforcement Learning via Confidence-Based Filters—0
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?—0
Safe Reinforcement Learning via Shielding under Partial Observability—0
Safe Reinforcement Learning with Probabilistic Control Barrier Functions for Ramp Merging—0
Safe Reinforcement Learning with Chance-constrained Model Predictive Control—0
Safe Reinforcement Learning with Contrastive Risk Prediction—0
Safe Reinforcement Learning with Dual Robustness—0
Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models—0
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints—0
Safe Reinforcement Learning with Linear Function Approximation—0
Safe Reinforcement Learning with Minimal Supervision—0
Safe Reinforcement Learning with Mixture Density Network: A Case Study in Autonomous Highway Driving—0
Safe Reinforcement Learning with Model Uncertainty Estimates—0
Safe Reinforcement Learning with Natural Language Constraints—0
An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems—0
Learning for MPC with Stability & Safety Guarantees—0
SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving—0
Safe Trajectory Planning Using Reinforcement Learning for Self Driving—0
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving—0
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Benchmark Results

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