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 3331–3340 of 15113 papers

TitleStatusHype
So You Think You Can Scale Up Autonomous Robot Data Collection?—0
Risk-sensitive control as inference with Rényi divergenceCode0
Simulation of Nanorobots with Artificial Intelligence and Reinforcement Learning for Advanced Cancer Cell Detection and TrackingCode0
N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs—0
Show, Don't Tell: Learning Reward Machines from Demonstrations for Reinforcement Learning-Based Cardiac Pacemaker Synthesis—0
GITSR: Graph Interaction Transformer-based Scene Representation for Multi Vehicle Collaborative Decision-making—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization—0
Hedging and Pricing Structured Products Featuring Multiple Underlying Assets—0
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data CorruptionsCode0
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Benchmark Results

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