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 3091–3100 of 15113 papers

TitleStatusHype
Bag of Policies for Distributional Deep Exploration—0
Revisiting a Design Choice in Gradient Temporal Difference Learning—0
Follow the Soldiers with Optimized Single-Shot Multibox Detection and Reinforcement Learning—0
qgym: A Gym for Training and Benchmarking RL-Based Quantum CompilationCode1
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman ProblemsCode1
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel OptimizationCode0
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges—0
End-to-End Reinforcement Learning for Torque Based Variable Height HoppingCode0
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route PredictionCode1
PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social DilemmasCode0
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Benchmark Results

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