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 9401–9425 of 15113 papers

TitleStatusHype
LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization—0
Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband—0
Weighted Entropy Modification for Soft Actor-Critic—0
Adaptive Contention Window Design using Deep Q-learningCode1
Counterfactual Credit Assignment in Model-Free Reinforcement Learning—0
C-Learning: Learning to Achieve Goals via Recursive Classification—0
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—0
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning—0
Modality-Buffet for Real-Time Object Detection—0
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation—0
REALab: An Embedded Perspective on Tampering—0
Reinforcement Learning of Graph Neural Networks for Service Function Chaining—0
Fault-Aware Robust Control via Adversarial Reinforcement Learning—0
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks—0
Curiosity Based Reinforcement Learning on Robot Manufacturing Cell—0
Leveraging the Variance of Return Sequences for Exploration Policy—0
Towards Learning Controllable Representations of Physical Systems—0
Towards a General Framework for ML-based Self-tuning Databases—0
Scalable Reinforcement Learning Policies for Multi-Agent ControlCode1
NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing TasksCode1
Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning—0
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Benchmark Results

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