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 3461–3470 of 15113 papers

TitleStatusHype
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning—0
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey—0
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic RewardsCode1
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning—0
Reinforcement Learning Approaches for Traffic Signal Control under Missing DataCode0
A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making—0
Bridging RL Theory and Practice with the Effective HorizonCode1
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing—0
End-to-End Policy Gradient Method for POMDPs and Explainable Agents—0
Sample-efficient Model-based Reinforcement Learning for Quantum ControlCode1
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Benchmark Results

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