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 8026–8050 of 15113 papers

TitleStatusHype
Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level—0
Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes—0
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences—0
Playing 2048 With Reinforcement LearningCode0
Transferring Reinforcement Learning for DC-DC Buck Converter Control via Duty Ratio Mapping: From Simulation to Implementation—0
Computationally Efficient Safe Reinforcement Learning for Power Systems—0
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning—0
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching—0
Feedback Linearization of Car Dynamics for Racing via Reinforcement Learning—0
Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space—0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization—0
Continuous Control with Action Quantization from Demonstrations—0
Aesthetic Photo Collage with Deep Reinforcement Learning—0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks—0
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes—0
State-based Episodic Memory for Multi-Agent Reinforcement Learning—0
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game—0
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm—0
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing—0
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs—0
Provable Hierarchy-Based Meta-Reinforcement Learning—0
Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition—0
Option Transfer and SMDP Abstraction with Successor Features—0
Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training—0
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Benchmark Results

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