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 3951–3960 of 15113 papers

TitleStatusHype
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
Derivative-Free Reinforcement Learning: A Review—0
Description Based Text Classification with Reinforcement Learning—0
Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes—0
Design and Development of Spoken Dialogue System in Indic Languages—0
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning—0
Design for a Darwinian Brain: Part 2. Cognitive Architecture—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
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Benchmark Results

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