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 1301–1310 of 15113 papers

TitleStatusHype
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes—0
Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning—0
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization—0
Navigation with QPHIL: Quantizing Planner for Hierarchical Implicit Q-Learning—0
QuadWBG: Generalizable Quadrupedal Whole-Body Grasping—0
Reinforcement learning for Quantum Tiq-Taq-ToeCode0
CROPS: A Deployable Crop Management System Over All Possible State Availabilities—0
Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings—0
Emergent Cooperative Strategies for Multi-Agent Shepherding via Reinforcement Learning—0
Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning—0
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Benchmark Results

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