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 6776–6800 of 15113 papers

TitleStatusHype
Neural Improvement Heuristics for Graph Combinatorial Optimization ProblemsCode0
The Phenomenon of Policy Churn—0
DM^2: Decentralized Multi-Agent Reinforcement Learning for Distribution MatchingCode0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Byzantine-Robust Online and Offline Distributed Reinforcement Learning—0
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL—0
A Database of Multimodal Data to Construct a Simulated Dialogue Partner with Varying Degrees of Cognitive Health—0
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the CloudCode0
A Mixture-of-Expert Approach to RL-based Dialogue Management—0
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning—0
Graph Backup: Data Efficient Backup Exploiting Markovian TransitionsCode0
k-Means Maximum Entropy Exploration—0
Provable General Function Class Representation Learning in Multitask Bandits and MDPs—0
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning—0
Multi-Agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP—0
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints—0
Robust Longitudinal Control for Vehicular Autonomous Platoons Using Deep Reinforcement Learning—0
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems—0
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game—0
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance LearningCode0
Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning—0
Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets—0
Reinforcement Learning with a TerminatorCode0
SEREN: Knowing When to Explore and When to Exploit—0
Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength—0
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Benchmark Results

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