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 2751–2800 of 15113 papers

TitleStatusHype
Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock PoolsCode1
Data-Driven LQR using Reinforcement Learning and Quadratic Neural Networks—0
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Runtime Verification of Learning Properties for Reinforcement Learning Algorithms—0
Reinforcement Learning with Model Predictive Control for Highway Ramp MeteringCode1
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling—0
Adversarial Imitation Learning On Aggregated Data—0
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk DecodingCode1
Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?—0
Workflow-Guided Response Generation for Task-Oriented Dialogue—0
When Mining Electric Locomotives Meet Reinforcement Learning—0
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations—0
An introduction to reinforcement learning for neuroscience—0
Reinforcement Learning for Solving Stochastic Vehicle Routing ProblemCode0
Learning Predictive Safety Filter via Decomposition of Robust Invariant Set—0
An advantage based policy transfer algorithm for reinforcement learning with measures of transferability—0
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
Clipped-Objective Policy Gradients for Pessimistic Policy OptimizationCode0
Out-of-Distribution-Aware Electric Vehicle Charging—0
Accelerating Exploration with Unlabeled Prior DataCode1
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence—0
LLM Augmented Hierarchical Agents—0
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control—0
Stable Modular Control via Contraction Theory for Reinforcement Learning—0
Low-Rank MDPs with Continuous Action Spaces—0
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy OptimizationCode1
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
High-dimensional Bid Learning for Energy Storage Bidding in Energy Markets—0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments—0
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models—0
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction EstimationCode1
Using General Value Functions to Learn Domain-Backed Inventory Management Policies—0
State-Wise Safe Reinforcement Learning With Pixel ObservationsCode1
Imitation Bootstrapped Reinforcement Learning—0
Towards model-free RL algorithms that scale well with unstructured data—0
Hierarchical Reinforcement Learning for Power Network Topology ControlCode1
Domain Randomization via Entropy Maximization—0
Energy Efficiency Optimization for Subterranean LoRaWAN Using A Reinforcement Learning Approach: A Direct-to-Satellite Scenario—0
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula—0
Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability—0
Diffusion Models for Reinforcement Learning: A SurveyCode2
Learning Realistic Traffic Agents in Closed-loop—0
Rethinking Decision Transformer via Hierarchical Reinforcement Learning—0
Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning—0
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning—0
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards—0
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving—0
A Tractable Inference Perspective of Offline RL—0
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Benchmark Results

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