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 3351–3375 of 15113 papers

TitleStatusHype
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
AIGenC: An AI generalisation model via creativity—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
DECORE: Deep Compression with Reinforcement Learning—0
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks—0
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning—0
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics—0
Decoupled Learning of Environment Characteristics for Safe Exploration—0
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning—0
Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks—0
A Tutorial Introduction to Reinforcement Learning—0
AI Planning: A Primer and Survey (Preliminary Report)—0
Covy: An AI-powered Robot with a Compound Vision System for Detecting Breaches in Social Distancing—0
A Study of State Aliasing in Structured Prediction with RNNs—0
COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning—0
Decoupling Strategy and Surface Realization for Task-oriented Dialogues—0
Cover Tree Bayesian Reinforcement Learning—0
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning—0
Accelerating the Learning of TAMER with Counterfactual Explanations—0
Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks—0
Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks—0
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Benchmark Results

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