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 11201–11225 of 15113 papers

TitleStatusHype
A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings—0
Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration—0
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization—0
Understanding & Generalizing AlphaGo Zero—0
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective—0
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage—0
Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization—0
Understanding Self-Predictive Learning for Reinforcement Learning—0
Understanding the Complexity Gains of Single-Task RL with a Curriculum—0
Understanding the Generalization Gap in Visual Reinforcement Learning—0
Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning—0
Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning—0
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
Show:102550
← PrevPage 449 of 605Next →

Benchmark Results

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