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 10626–10650 of 15113 papers

TitleStatusHype
Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach—0
Tangent Space Least Adaptive Clustering—0
TAP-Net: Transport-and-Pack using Reinforcement Learning—0
CopyCAT: Taking Control of Neural Policies with Constant Attacks—0
Targeted Data Acquisition for Evolving Negotiation Agents—0
Targeted Environment Design from Offline Data—0
Target-independent XLA optimization using Reinforcement Learning—0
Target Network and Truncation Overcome The Deadly Triad in Q-Learning—0
Targets in Reinforcement Learning to solve Stackelberg Security Games—0
Target Transfer Q-Learning and Its Convergence Analysis—0
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification—0
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control—0
Task-agnostic Exploration in Reinforcement Learning—0
Task-agnostic Pre-training and Task-guided Fine-tuning for Versatile Diffusion Planner—0
Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network—0
Task-driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning—0
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments—0
Task-Guided Inverse Reinforcement Learning Under Partial Information—0
Task Independent Capsule-Based Agents for Deep Q-Learning—0
Task-Induced Representation Learning—0
Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures—0
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization—0
Task-Oriented Communication Design at Scale—0
Task-oriented Design through Deep Reinforcement Learning—0
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Benchmark Results

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