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

Showing 1235112400 of 15113 papers

TitleStatusHype
Sequential Anomaly Detection using Inverse Reinforcement Learning0
Sequential Attacks on Agents for Long-Term Adversarial Goals0
Sequential Bayesian experimental designs via reinforcement learning0
Sequential Communication in Multi-Agent Reinforcement Learning0
Sequential Cost-Sensitive Feature Acquisition0
Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation0
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget0
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning0
Sequential Search with Off-Policy Reinforcement Learning0
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning0
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling0
Sequential Transfer in Multi-armed Bandit with Finite Set of Models0
Sequential Transfer in Reinforcement Learning with a Generative Model0
Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies0
Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration0
SEREN: Knowing When to Explore and When to Exploit0
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning0
FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimize0
Setting up experimental Bell test with reinforcement learning0
Achieving Constant Regret in Linear Markov Decision Processes0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning0
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning0
Settling the Sample Complexity of Model-Based Offline Reinforcement Learning0
Settling the Sample Complexity of Online Reinforcement Learning0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning0
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training0
Shallow Updates for Deep Reinforcement Learning0
Shaped Policy Search for Evolutionary Strategies using Waypoints0
Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning0
Shaping a social robot's humor with Natural Language Generation and socially-aware reinforcement learning0
Shaping Belief States with Generative Environment Models for RL0
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning0
Shared Learning : Enhancing Reinforcement in Q-Ensembles0
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF0
Sharp Analysis of Smoothed Bellman Error Embedding0
Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes0
Safe Reinforcement Learning via Probabilistic Shields0
ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers0
SHIRO: Soft Hierarchical Reinforcement Learning0
Short Quantum Circuits in Reinforcement Learning Policies for the Vehicle Routing Problem0
Should I Run Offline Reinforcement Learning or Behavioral Cloning?0
Should I send this notification? Optimizing push notifications decision making by modeling the future0
Should we use model-free or model-based control? A case study of battery management systems0
Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network0
Show, Don't Tell: Learning Reward Machines from Demonstrations for Reinforcement Learning-Based Cardiac Pacemaker Synthesis0
Showing versus doing: Teaching by demonstration0
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters0
Show me the Way: Intrinsic Motivation from Demonstrations0
Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstration0
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Benchmark Results

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