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 2291–2300 of 15113 papers

TitleStatusHype
Constructing Non-Markovian Decision Process via History AggregatorCode0
Listener-Rewarded Thinking in VLMs for Image Preferences—0
A Survey of Continual Reinforcement Learning—0
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review—0
Homogenization of Multi-agent Learning Dynamics in Finite-state Markov GamesCode0
RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment—0
Optimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage—0
Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning—0
Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning—0
Curriculum-Guided Antifragile Reinforcement Learning for Secure UAV Deconfliction under Observation-Space Attacks—0
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Benchmark Results

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