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 3271–3280 of 15113 papers

TitleStatusHype
A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback—0
Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning—0
ACING: Actor-Critic for Instruction Learning in Black-Box Large Language ModelsCode0
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning—0
Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning—0
Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of BeamlinesCode0
Preserving Expert-Level Privacy in Offline Reinforcement Learning—0
Upside-Down Reinforcement Learning for More Interpretable Optimal Control—0
Regret-Free Reinforcement Learning for LTL Specifications—0
Enhancing Decision Transformer with Diffusion-Based Trajectory Branch Generation—0
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Benchmark Results

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