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 5276–5300 of 15113 papers

TitleStatusHype
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning—0
Reversible Upper Confidence Bound Algorithm to Generate Diverse Optimized Candidates—0
Review, Analysis and Design of a Comprehensive Deep Reinforcement Learning Framework—0
Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning—0
Revised Progressive-Hedging-Algorithm Based Two-layer Solution Scheme for Bayesian Reinforcement Learning—0
Revisiting Design Choices in Offline Model-Based Reinforcement Learning—0
Revisiting Design Choices in Offline Model Based Reinforcement Learning—0
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning—0
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach—0
Revisiting Peng's Q(λ) for Modern Reinforcement Learning—0
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning—0
Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous—0
Offline Reinforcement Learning via Linear-Programming with Error-Bound Induced Constraints—0
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning—0
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
Revisiting the Roles of “Text” in Text Games—0
Revisiting the Roles of "Text" in Text Games—0
Revolutionizing Genomics with Reinforcement Learning Techniques—0
REvolve: Reward Evolution with Large Language Models using Human Feedback—0
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning—0
Reward-Aware Proto-Representations in Reinforcement Learning—0
Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Reward Constrained Interactive Recommendation with Natural Language Feedback—0
Show:102550
← PrevPage 212 of 605Next →

Benchmark Results

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