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 4341–4350 of 15113 papers

TitleStatusHype
Efficient Meta Reinforcement Learning for Preference-based Fast AdaptationCode1
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value FunctionCode1
Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning—0
Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design—0
ReInform: Selecting paths with reinforcement learning for contextualized link predictionCode0
LibSignal: An Open Library for Traffic Signal ControlCode1
Analysis of Reinforcement Learning Schemes for Trajectory Optimization of an Aerial Radio Unit—0
Provable Defense against Backdoor Policies in Reinforcement LearningCode0
Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action CorrectionsCode1
Credit-cognisant reinforcement learning for multi-agent cooperation—0
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Benchmark Results

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