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 2951–2975 of 15113 papers

TitleStatusHype
A Fast Convergence Theory for Offline Decision Making—0
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search—0
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning—0
Decoupled Learning of Environment Characteristics for Safe Exploration—0
A Theory of Abstraction in Reinforcement Learning—0
A Theoretical Connection Between Statistical Physics and Reinforcement Learning—0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes—0
A Human Mixed Strategy Approach to Deep Reinforcement Learning—0
Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Uncertain Nonlinear Systems—0
A Tensor Network Approach to Finite Markov Decision Processes—0
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning—0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
A Temporal Difference Reinforcement Learning Theory of Emotion: unifying emotion, cognition and adaptive behavior—0
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming—0
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations—0
ACDER: Augmented Curiosity-Driven Experience Replay—0
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning—0
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration—0
Decoupling Strategy and Surface Realization for Task-oriented Dialogues—0
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning—0
A Technical Study into Small Reasoning Language Models—0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
A Teacher-Student Framework for Maintainable Dialog Manager—0
A Taxonomy of Similarity Metrics for Markov Decision Processes—0
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Benchmark Results

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