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 6926–6950 of 15113 papers

TitleStatusHype
Context Meta-Reinforcement Learning via NeuromodulationCode0
On Joint Learning for Solving Placement and Routing in Chip DesignCode1
Reinforced Workload Distribution Fairness—0
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning—0
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System—0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL—0
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning—0
Adaptive Discretization in Online Reinforcement Learning—0
Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control—0
Open Problem: Tight Online Confidence Intervals for RKHS Elements—0
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision ProcessesCode0
Efficient Meta Subspace OptimizationCode0
URLB: Unsupervised Reinforcement Learning BenchmarkCode1
D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning—0
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples—0
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives—0
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning—0
Extracting Expert's Goals by What-if Interpretable Modeling—0
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection—0
Stabilising viscous extensional flows using Reinforcement LearningCode0
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—0
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Benchmark Results

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