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 9126–9150 of 15113 papers

TitleStatusHype
Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems—0
Curriculum-based Deep Reinforcement Learning for Quantum Control—0
Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning—0
Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup—0
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient OptimizationCode1
Relational Deep Reinforcement Learning for Routing in Wireless Networks—0
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration—0
Model Free Reinforcement Learning Algorithm for Stationary Mean field Equilibrium for Multiple Types of Agents—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Model-Based Visual Planning with Self-Supervised Functional DistancesCode1
Is Pessimism Provably Efficient for Offline RL?—0
Reinforcement Learning for Control of ValvesCode1
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning—0
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing—0
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy—0
Portfolio Optimization with 2D Relative-Attentional Gated Transformer—0
Stability-Certified Reinforcement Learning via Spectral Normalization—0
POPO: Pessimistic Offline Policy OptimizationCode0
Towards sample-efficient episodic control with DAC-ML—0
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device—0
Deep Reinforcement Learning for Long-Short Portfolio OptimizationCode0
Towards Continual Reinforcement Learning: A Review and Perspectives—0
A State Representation Dueling Network for Deep Reinforcement Learning—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search—0
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Benchmark Results

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