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

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Showing 1691–1700 of 15113 papers

TitleStatusHype
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds—0
Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space ProgramCode1
CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk—0
Neural Reward MachinesCode0
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct OptimizationCode1
Online Behavior Modification for Expressive User Control of RL-Trained Robots—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
Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx—0
Off-Policy Reinforcement Learning with High Dimensional Reward—0
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Benchmark Results

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