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 1001–1010 of 15113 papers

TitleStatusHype
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
Model-Free RL Agents Demonstrate System 1-Like Intentionality—0
Neural Operator based Reinforcement Learning for Control of first-order PDEs with Spatially-Varying State DelayCode0
RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems—0
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information—0
From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning—0
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic LearningCode1
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled PlatformsCode0
Integrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care—0
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Benchmark Results

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