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 8001–8025 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments—0
Operator Shifting for Model-based Policy Evaluation—0
Mixture-of-Variational-Experts for Continual LearningCode0
Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control TasksCode0
Self-Consistent Models and Values—0
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning—0
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks—0
Foresight of Graph Reinforcement Learning Latent Permutations Learnt by Gumbel Sinkhorn Network—0
Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Policy Search using Dynamic Mirror Descent MPC for Model Free Off Policy RL—0
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction—0
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive ModelsCode0
Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing—0
Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer—0
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks—0
Is High Variance Unavoidable in RL? A Case Study in Continuous Control—0
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information—0
Can Q-learning solve Multi Armed Bantids?—0
Anti-Concentrated Confidence Bonuses for Scalable Exploration—0
Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations—0
Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain—0
Neuro-Symbolic Reinforcement Learning with First-Order Logic—0
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Benchmark Results

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