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 5926–5950 of 15113 papers

TitleStatusHype
Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies—0
Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration—0
SEREN: Knowing When to Explore and When to Exploit—0
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning—0
FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimize—0
Setting up experimental Bell test with reinforcement learning—0
Achieving Constant Regret in Linear Markov Decision Processes—0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning—0
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning—0
Settling the Sample Complexity of Model-Based Offline Reinforcement Learning—0
Settling the Sample Complexity of Online Reinforcement Learning—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training—0
Shallow Updates for Deep Reinforcement Learning—0
Shaped Policy Search for Evolutionary Strategies using Waypoints—0
Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning—0
Shaping a social robot's humor with Natural Language Generation and socially-aware reinforcement learning—0
Shaping Belief States with Generative Environment Models for RL—0
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models—0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
Shared Learning : Enhancing Reinforcement in Q-Ensembles—0
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF—0
Sharp Analysis of Smoothed Bellman Error Embedding—0
Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes—0
Safe Reinforcement Learning via Probabilistic Shields—0
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Benchmark Results

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