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 5551–5575 of 15113 papers

TitleStatusHype
Route Optimization via Environment-Aware Deep Network and Reinforcement Learning—0
Routing algorithms as tools for integrating social distancing with emergency evacuation—0
Routing and Placement of Macros using Deep Reinforcement Learning—0
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning—0
RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature Selection—0
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels—0
Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning—0
Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction—0
Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agents—0
Rule Mining over Knowledge Graphs via Reinforcement Learning—0
Run-and-tumble chemotaxis using reinforcement learning—0
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning—0
Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection—0
Runtime Safety Assurance Using Reinforcement Learning—0
Runtime Verification of Learning Properties for Reinforcement Learning Algorithms—0
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
S2VG: Soft Stochastic Value Gradient method—0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning—0
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics—0
SAC-GLAM: Improving Online RL for LLM agents with Soft Actor-Critic and Hindsight Relabeling—0
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons—0
Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking—0
Safe and Robust Reinforcement Learning: Principles and Practice—0
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation—0
Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics—0
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Benchmark Results

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