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 781790 of 15113 papers

TitleStatusHype
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D UltrasoundCode1
Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement LearningCode1
A2C is a special case of PPOCode1
CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory OptimizationCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
CaiRL: A High-Performance Reinforcement Learning Environment ToolkitCode1
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
Efficient Wasserstein Natural Gradients for Reinforcement LearningCode1
Deep Transformer Q-Networks for Partially Observable Reinforcement LearningCode1
Active Inference for Stochastic ControlCode1
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Benchmark Results

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