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 1501–1510 of 15113 papers

TitleStatusHype
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy OptimizationCode1
Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement LearningCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
Uncertainty Weighted Actor-Critic for Offline Reinforcement LearningCode1
An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with PybulletCode1
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation PerspectiveCode1
Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge GraphsCode1
A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless SystemsCode1
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-ResolutionCode1
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Benchmark Results

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