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 4151–4160 of 15113 papers

TitleStatusHype
Horizon-Free Regret for Linear Markov Decision Processes—0
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning—0
Meta-operators for Enabling Parallel Planning Using Deep Reinforcement Learning—0
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning—0
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis—0
TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement LearningCode0
Learning to Describe for Predicting Zero-shot Drug-Drug InteractionsCode0
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback—0
Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter ControlCode0
A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware PerspectiveCode0
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Benchmark Results

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