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 7276–7300 of 15113 papers

TitleStatusHype
Combinatorial Reinforcement Learning Based Scheduling for DNN Execution on Edge—0
Hypothesis Driven Coordinate Ascent for Reinforcement Learning—0
Adversarial Style Transfer for Robust Policy Optimization in Reinforcement Learning—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Fourier Features in Reinforcement Learning with Neural NetworksCode0
AARL: Automated Auxiliary Loss for Reinforcement Learning—0
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management—0
Rewardless Open-Ended Learning (ROEL)—0
The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning—0
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers—0
Offline Reinforcement Learning with Resource Constrained Online Deployment—0
Offline Reinforcement Learning with In-sample Q-LearningCode1
Pretraining for Language Conditioned Imitation with Transformers—0
Reasoning With Hierarchical Symbols: Reclaiming Symbolic Policies For Visual Reinforcement Learning—0
PDQN - A Deep Reinforcement Learning Method for Planning with Long Delays: Optimization of Manufacturing Dispatching—0
Theoretical understanding of adversarial reinforcement learning via mean-field optimal control—0
Pareto Policy Adaptation—0
SPP-RL: State Planning Policy Reinforcement Learning—0
Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems—0
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm—0
^2-exploration for Reinforcement Learning—0
MOBA: Multi-teacher Model Based Reinforcement Learning—0
Rethinking Pareto Approaches in Constrained Reinforcement Learning—0
Reinforcement Learning with Ex-Post Max-Min Fairness—0
Weakly-Supervised Learning of Disentangled and Interpretable Skills for Hierarchical Reinforcement Learning—0
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Benchmark Results

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