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 1271–1280 of 15113 papers

TitleStatusHype
ACING: Actor-Critic for Instruction Learning in Black-Box Large Language ModelsCode0
Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of BeamlinesCode0
Preserving Expert-Level Privacy in Offline Reinforcement Learning—0
Regret-Free Reinforcement Learning for LTL Specifications—0
Enhancing Decision Transformer with Diffusion-Based Trajectory Branch Generation—0
Continual Task Learning through Adaptive Policy Self-CompositionCode0
No-regret Exploration in Shuffle Private Reinforcement Learning—0
Upside-Down Reinforcement Learning for More Interpretable Optimal Control—0
Robust Reinforcement Learning under Diffusion Models for Data with Jumps—0
Financial News-Driven LLM Reinforcement Learning for Portfolio Management—0
Show:102550
← PrevPage 128 of 1512Next →

Benchmark Results

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