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 3641–3650 of 15113 papers

TitleStatusHype
Vanilla Gradient Descent for Oblique Decision TreesCode0
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds—0
Neural Reward MachinesCode0
CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk—0
BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination—0
Experimental evaluation of offline reinforcement learning for HVAC control in buildingsCode0
Online Behavior Modification for Expressive User Control of RL-Trained Robots—0
Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx—0
Off-Policy Reinforcement Learning with High Dimensional Reward—0
Large Language Models Prompting With Episodic Memory—0
Show:102550
← PrevPage 365 of 1512Next →

Benchmark Results

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