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 8326–8350 of 15113 papers

TitleStatusHype
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory—0
Stability and Generalisation in Batch Reinforcement Learning—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
WaveCorr: Deep Reinforcement Learning with Permutation Invariant Policy Networks for Portfolio Management—0
On Reward Maximization and Distribution Matching for Fine-Tuning Language Models—0
Weakly-Supervised Learning of Disentangled and Interpretable Skills for Hierarchical Reinforcement Learning—0
SPP-RL: State Planning Policy Reinforcement Learning—0
On the benefits of deep RL in accelerated MRI sampling—0
Pareto Policy Adaptation—0
Nested Policy Reinforcement Learning for Clinical Decision Support—0
Pareto Policy Pool for Model-based Offline Reinforcement Learning—0
Programmatic Reinforcement Learning without Oracles—0
Neural Combinatorial Optimization with Reinforcement Learning : Solving theVehicle Routing Problem with Time Windows—0
Particle Based Stochastic Policy Optimization—0
Why so pessimistic? Estimating uncertainties for offline RL through ensembles, and why their independence matters.—0
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics—0
Reinforcement Learning with Ex-Post Max-Min Fairness—0
Zero-Shot Reward Specification via Grounded Natural Language—0
LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning—0
Making Curiosity Explicit in Vision-based RL—0
Reinforcement Learning for Quantitative Trading—0
A First-Occupancy Representation for Reinforcement Learning—0
Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey—0
Longitudinal Deep Truck: Deep learning and deep reinforcement learning for modeling and control of longitudinal dynamics of heavy duty trucks—0
Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing—0
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Benchmark Results

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