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 6976–7000 of 15113 papers

TitleStatusHype
Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning—0
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning—0
Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence—0
Undirected Machine Translation with Discriminative Reinforcement Learning—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution—0
Reinforcement Learning in Credit Scoring and Underwriting—0
UniCon: Universal Neural Controller For Physics-based Character Motion—0
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning—0
Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents—0
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games—0
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems—0
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation—0
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension—0
Uniform State Abstraction For Reinforcement Learning—0
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory—0
Unifying Ensemble Methods for Q-learning via Social Choice Theory—0
Unifying task specification in reinforcement learning—0
Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming—0
Universal Activation Function For Machine Learning—0
Universal Agent for Disentangling Environments and Tasks—0
Show:102550
← PrevPage 280 of 605Next →

Benchmark Results

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