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 3351–3400 of 15113 papers

TitleStatusHype
A Gentle Lecture Note on Filtrations in Reinforcement Learning—0
AIGenC: An AI generalisation model via creativity—0
A Subgame Perfect Equilibrium Reinforcement Learning Approach to Time-inconsistent Problems—0
Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents—0
AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning—0
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning—0
Capturing Financial markets to apply Deep Reinforcement Learning—0
DECORE: Deep Compression with Reinforcement Learning—0
Deep reinforcement learning in medical imaging: A literature review—0
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics—0
Decoupled Learning of Environment Characteristics for Safe Exploration—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration—0
A Tutorial Introduction to Reinforcement Learning—0
AI Planning: A Primer and Survey (Preliminary Report)—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks—0
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning—0
Decoupling Strategy and Surface Realization for Task-oriented Dialogues—0
Deep Reinforcement Learning in a Monetary Model—0
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning—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
Deep Actor-Critic Reinforcement Learning for Anomaly Detection—0
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
DEEP ADVERSARIAL FORWARD MODEL—0
Deep Adversarial Reinforcement Learning for Object Disentangling—0
DeepAGREL: Biologically plausible deep learning via direct reinforcement—0
Deep Anomaly Detection and Search via Reinforcement Learning—0
Deep Apprenticeship Learning for Playing Games—0
Deep-Attack over the Deep Reinforcement Learning—0
COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning—0
Adaptive Control of Differentially Private Linear Quadratic Systems—0
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework—0
Deep Bellman Hedging—0
Deep Binary Reinforcement Learning for Scalable Verification—0
Cover Tree Bayesian Reinforcement Learning—0
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling—0
Deep Coherent Exploration For Continuous Control—0
Accelerating the Learning of TAMER with Counterfactual Explanations—0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey—0
Augmenting Automated Game Testing with Deep Reinforcement Learning—0
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks—0
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games—0
Deep Curiosity Loops in Social Environments—0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability—0
Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming—0
Deep differentiable reinforcement learning and optimal trading—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
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Benchmark Results

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