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 8951–8975 of 15113 papers

TitleStatusHype
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs—0
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning in Quantitative Algorithmic Trading: A ReviewCode0
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning—0
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture—0
Shaped Policy Search for Evolutionary Strategies using Waypoints—0
On the Theory of Reinforcement Learning with Once-per-Episode Feedback—0
Predictive Representation Learning for Language Modeling—0
Gradient-Free Neural Network Training via Synaptic-Level Reinforcement Learning—0
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles—0
Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm—0
Reconfigurable Intelligent Surface-assisted Multi-UAV Networks: Efficient Resource Allocation with Deep Reinforcement Learning—0
Learning Approximate and Exact Numeral Systems via Reinforcement Learning—0
A nearly Blackwell-optimal policy gradient methodCode0
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model—0
Risk-Aware Transfer in Reinforcement Learning using Successor Features—0
Task-Guided Inverse Reinforcement Learning Under Partial Information—0
Reinforcement Learning reveals fundamental limits on the mixing of active particles—0
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications—0
Stochastic Intervention for Causal Inference via Reinforcement Learning—0
Reinforcement Learning for on-line Sequence Transformation—0
Optimistic Reinforcement Learning by Forward Kullback-Leibler Divergence Optimization—0
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching—0
Branching Dueling Q-Network Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems—0
Adversarial Intrinsic Motivation for Reinforcement LearningCode0
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Benchmark Results

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