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 3031–3040 of 15113 papers

TitleStatusHype
A Survey on Traffic Signal Control Methods—0
A gray-box approach for curriculum learning—0
A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances—0
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications—0
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study—0
A Survey on Reinforcement Learning Security with Application to Autonomous Driving—0
A Survey on Reinforcement Learning Methods in Character Animation—0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning—0
The Principle of Unchanged Optimality in Reinforcement Learning Generalization—0
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Benchmark Results

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