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 5376–5400 of 15113 papers

TitleStatusHype
Risk-sensitive Reinforcement Learning—0
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path—0
Risk-Sensitive Reinforcement Learning via Policy Gradient Search—0
Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty—0
Risk-Sensitive Reinforcement Learning Applied to Control under Constraints—0
Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions—0
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret—0
Risk-Sensitive Reinforcement Learning with Exponential Criteria—0
RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations—0
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks—0
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models—0
RL4ReAl: Reinforcement Learning for Register Allocation—0
RLAD: Reinforcement Learning from Pixels for Autonomous Driving in Urban Environments—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents—0
R-LAtte: Attention Module for Visual Control via Reinforcement Learning—0
RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption—0
RL-based Control of UAS Subject to Significant Disturbance—0
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies—0
RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems—0
RLCache: Automated Cache Management Using Reinforcement Learning—0
RLCAD: Reinforcement Learning Training Gym for Revolution Involved CAD Command Sequence Generation—0
RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning—0
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning—0
RL-Controller: a reinforcement learning framework for active structural control—0
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Benchmark Results

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