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 9801–9825 of 15113 papers

TitleStatusHype
Model-Free Reinforcement Learning for Asset Allocation—0
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation—0
Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications—0
Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization—0
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity—0
Model-free Representation Learning and Exploration in Low-rank MDPs—0
Model-Free Risk-Sensitive Reinforcement Learning—0
Model-Free RL Agents Demonstrate System 1-Like Intentionality—0
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees—0
Model-Free Unsupervised Learning for Optimization Problems with Constraints—0
Model Generation with Provable Coverability for Offline Reinforcement Learning—0
Model Imitation for Model-Based Reinforcement Learning—0
Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach—0
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning—0
Modeling Cyber-Physical Human Systems via an Interplay Between Reinforcement Learning and Game Theory—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention—0
Modeling Human Reading with Neural Attention—0
Modeling human road crossing decisions as reward maximization with visual perception limitations—0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance—0
Modeling Mobile Health Users as Reinforcement Learning Agents—0
Modeling Others using Oneself in Multi-Agent Reinforcement Learning—0
Modeling Risk in Reinforcement Learning: A Literature Mapping—0
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication—0
Modeling Survival in model-based Reinforcement Learning—0
Show:102550
← PrevPage 393 of 605Next →

Benchmark Results

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