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 10701–10725 of 15113 papers

TitleStatusHype
Synthesizing Programmatic Policies that Inductively Generalize—0
AMRL: Aggregated Memory For Reinforcement Learning—0
Episodic Reinforcement Learning with Associative Memory—0
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning—0
Learning Collaborative Agents with Rule Guidance for Knowledge Graph ReasoningCode1
Improving Robustness via Risk Averse Distributional Reinforcement Learning—0
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?—0
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning—0
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning—0
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning—0
Improving Factual Consistency Between a Response and Persona Facts—0
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging—0
DSAC: Distributional Soft Actor Critic for Risk-Sensitive Reinforcement Learning—0
Out-of-the-box channel pruned networks—0
Towards Embodied Scene Description—0
Reinforcement learning of minimalist grammars—0
Plan-Space State Embeddings for Improved Reinforcement Learning—0
Unsupervised Learning of KB Queries in Task-Oriented Dialogs—0
Reinforcement Learning with Augmented DataCode1
Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations—0
Whittle index based Q-learning for restless bandits with average reward—0
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks—0
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning—0
Hierarchical Reinforcement Learning for Automatic Disease DiagnosisCode1
Actor-Critic Reinforcement Learning for Control with Stability GuaranteeCode1
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Benchmark Results

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