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 7101–7125 of 15113 papers

TitleStatusHype
V2N Service Scaling with Deep Reinforcement Learning—0
VacciNet: Towards a Smart Framework for Learning the Distribution Chain Optimization of Vaccines for a Pandemic—0
Vairiational Stochastic Games—0
Validation of massively-parallel adaptive testing using dynamic control matching—0
Value-Added Chemical Discovery Using Reinforcement Learning—0
Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning—0
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems—0
Bayesian Meta-reinforcement Learning for Traffic Signal Control—0
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings—0
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning—0
Value-driven Hindsight Modelling—0
Value Driven Representation for Human-in-the-Loop Reinforcement Learning—0
Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization—0
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents—0
Instance-dependent _-bounds for policy evaluation in tabular reinforcement learning—0
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning—0
Dexterous In-hand Manipulation by Guiding Exploration with Simple Sub-skill Controllers—0
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF—0
Value of Information and Reward Specification in Active Inference and POMDPs—0
Value Penalized Q-Learning for Recommender Systems—0
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning—0
Value Propagation Networks—0
Value Pursuit Iteration—0
Value Refinement Network (VRN)—0
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Benchmark Results

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