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 2721–2730 of 15113 papers

TitleStatusHype
Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks—0
Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control—0
Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes—0
CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent—0
Efficient Implementation of Reinforcement Learning over Homomorphic Encryption—0
Towards Optimal Differentially Private Regret Bounds in Linear MDPs—0
Development of a PPO-Reinforcement Learned Walking Tripedal Soft-Legged Robot using SOFACode0
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making—0
Deep Distributional Learning with Non-crossing Quantile Network—0
Spectral Normalization for Lipschitz-Constrained Policies on Learning Humanoid Locomotion—0
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Benchmark Results

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