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 8126–8150 of 15113 papers

TitleStatusHype
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs—0
Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks—0
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning—0
Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning—0
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning—0
Deep Reinforcement Learning in Quantitative Algorithmic Trading: A ReviewCode0
Q-attention: Enabling Efficient Learning for Vision-based Robotic ManipulationCode1
Shaped Policy Search for Evolutionary Strategies using Waypoints—0
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture—0
Predictive Representation Learning for Language Modeling—0
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles—0
Gradient-Free Neural Network Training via Synaptic-Level Reinforcement Learning—0
On the Theory of Reinforcement Learning with Once-per-Episode Feedback—0
Reinforcement Learning for on-line Sequence Transformation—0
Reinforcement Learning reveals fundamental limits on the mixing of active particles—0
Reconfigurable Intelligent Surface-assisted Multi-UAV Networks: Efficient Resource Allocation with Deep Reinforcement Learning—0
Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm—0
A nearly Blackwell-optimal policy gradient methodCode0
Learning Approximate and Exact Numeral Systems via Reinforcement Learning—0
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics MixtureCode1
Stochastic Intervention for Causal Inference via Reinforcement Learning—0
Task-Guided Inverse Reinforcement Learning Under Partial Information—0
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications—0
Towards mental time travel: a hierarchical memory for reinforcement learning agentsCode1
Show:102550
← PrevPage 326 of 605Next →

Benchmark Results

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