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 2281–2290 of 15113 papers

TitleStatusHype
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Finite-Time Analysis of Temporal Difference Learning: Discrete-Time Linear System Perspective—0
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL—0
Behaviorally Diverse Traffic Simulation via Reinforcement Learning—0
Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion—0
Computational Co-Design for Variable Geometry Truss—0
A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design—0
Behavior Alignment via Reward Function Optimization—0
Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement Learning—0
Analysis of Stochastic Processes through Replay Buffers—0
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Benchmark Results

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