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

Showing 80018050 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments0
Operator Shifting for Model-based Policy Evaluation0
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 Values0
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning0
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks0
Foresight of Graph Reinforcement Learning Latent Permutations Learnt by Gumbel Sinkhorn Network0
Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits0
Policy Search using Dynamic Mirror Descent MPC for Model Free Off Policy RL0
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction0
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive ModelsCode0
Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing0
Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer0
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow0
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks0
Is High Variance Unavoidable in RL? A Case Study in Continuous Control0
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information0
Can Q-learning solve Multi Armed Bantids?0
Anti-Concentrated Confidence Bonuses for Scalable Exploration0
Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations0
Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain0
Neuro-Symbolic Reinforcement Learning with First-Order Logic0
Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level0
Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes0
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences0
Playing 2048 With Reinforcement LearningCode0
Transferring Reinforcement Learning for DC-DC Buck Converter Control via Duty Ratio Mapping: From Simulation to Implementation0
Computationally Efficient Safe Reinforcement Learning for Power Systems0
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning0
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching0
Feedback Linearization of Car Dynamics for Racing via Reinforcement Learning0
Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization0
Continuous Control with Action Quantization from Demonstrations0
Aesthetic Photo Collage with Deep Reinforcement Learning0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks0
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes0
State-based Episodic Memory for Multi-Agent Reinforcement Learning0
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game0
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm0
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing0
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs0
Provable Hierarchy-Based Meta-Reinforcement Learning0
Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition0
Option Transfer and SMDP Abstraction with Successor Features0
Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training0
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Benchmark Results

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