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 6476–6500 of 15113 papers

TitleStatusHype
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning—0
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning—0
Actor-Critic Network for Q&A in an Adversarial Environment—0
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
Robust Algorithmic Collusion—0
Toward Causal-Aware RL: State-Wise Action-Refined Temporal DifferenceCode0
Reinforcement Learning for Task Specifications with Action-Constraints—0
Temporal Complementarity-Guided Reinforcement Learning for Image-to-Video Person Re-Identification—0
Symmetry-Aware Neural Architecture for Embodied Visual Exploration—0
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning—0
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning—0
Transfer RL across Observation Feature Spaces via Model-Based Regularization—0
Stochastic convex optimization for provably efficient apprenticeship learning—0
Using Graph-Aware Reinforcement Learning to Identify Winning Strategies in Diplomacy Games (Student Abstract)—0
Single-Shot Pruning for Offline Reinforcement Learning—0
Robust Entropy-regularized Markov Decision Processes—0
SimSR: Simple Distance-based State Representation for Deep Reinforcement LearningCode1
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement LearningCode0
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning—0
Stability-Preserving Automatic Tuning of PID Control with Reinforcement Learning—0
Reversible Upper Confidence Bound Algorithm to Generate Diverse Optimized Candidates—0
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation—0
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates—0
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Benchmark Results

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