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 5951–5975 of 15113 papers

TitleStatusHype
ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers—0
SHIRO: Soft Hierarchical Reinforcement Learning—0
Short Quantum Circuits in Reinforcement Learning Policies for the Vehicle Routing Problem—0
Should I Run Offline Reinforcement Learning or Behavioral Cloning?—0
Should I send this notification? Optimizing push notifications decision making by modeling the future—0
Should we use model-free or model-based control? A case study of battery management systems—0
Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network—0
Show, Don't Tell: Learning Reward Machines from Demonstrations for Reinforcement Learning-Based Cardiac Pacemaker Synthesis—0
Showing versus doing: Teaching by demonstration—0
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters—0
Show me the Way: Intrinsic Motivation from Demonstrations—0
Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstration—0
Show Us the Way: Learning to Manage Dialog from Demonstrations—0
Shrinkage-based Bias-Variance Trade-off for Deep Reinforcement Learning—0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
SIBRE: Self Improvement Based REwards for Adaptive Feedback in Reinforcement Learning—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning—0
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning—0
Signal Temporal Logic Neural Predictive Control—0
Sign and Relevance Learning—0
Sim2real for Reinforcement Learning Driven Next Generation Networks—0
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach—0
Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)—0
SIMILE: Introducing Sequential Information towards More Effective Imitation Learning—0
Show:102550
← PrevPage 239 of 605Next →

Benchmark Results

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