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

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Showing 8651–8675 of 15113 papers

TitleStatusHype
Foresee then Evaluate: Decomposing Value Estimation with Latent Future PredictionCode0
Adversarial Environment Generation for Learning to Navigate the WebCode0
Minimax Model Learning—0
Offline Reinforcement Learning with Pseudometric Learning—0
Model-based Constrained Reinforcement Learning using Generalized Control Barrier FunctionCode1
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent GamesCode1
Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB trafficCode1
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational GraphCode1
Reinforcement Learning for Adaptive Mesh Refinement—0
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach—0
Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning—0
Hamiltonian Policy Optimization—0
Learning for Visual Navigation by Imagining the Success—0
Exploration and Incentives in Reinforcement Learning—0
Where the Action is: Let's make Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problems work!—0
Reducing Conservativeness Oriented Offline Reinforcement Learning—0
Optimal control of point-to-point navigation in turbulent time-dependent flows using Reinforcement Learning—0
Revisiting Peng's Q(λ) for Modern Reinforcement Learning—0
Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction ReportCode1
Safe Distributional Reinforcement Learning—0
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision—0
Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online PlanningCode1
On the Importance of Hyperparameter Optimization for Model-based Reinforcement LearningCode1
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information BottleneckCode0
Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach—0
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Benchmark Results

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