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 10976–11000 of 15113 papers

TitleStatusHype
Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks—0
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making—0
Towards More Theoretically-Grounded Particle Optimization Sampling for Deep Learning—0
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines—0
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations—0
Towards Neural Machine Translation with Latent Tree Attention—0
Towards one-shot learning for rare-word translation with external experts—0
Towards on-sky adaptive optics control using reinforcement learning—0
Towards Optimal Differentially Private Regret Bounds in Linear MDPs—0
Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning—0
Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning—0
Towards Optimal Pricing of Demand Response -- A Nonparametric Constrained Policy Optimization Approach—0
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning—0
Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest—0
Towards Physically Safe Reinforcement Learning under Supervision—0
Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer—0
Towards Playing Full MOBA Games with Deep Reinforcement Learning—0
Towards Practical Credit Assignment for Deep Reinforcement Learning—0
Towards Practical Deep Schedulers for Allocating Cellular Radio Resources—0
Towards practical reinforcement learning for tokamak magnetic control—0
Towards Quantum-Enabled 6G Slicing—0
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer—0
Towards Resolving Unidentifiability in Inverse Reinforcement Learning—0
Towards robust and domain agnostic reinforcement learning competitions—0
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Benchmark Results

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