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 2741–2750 of 15113 papers

TitleStatusHype
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model—0
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints—0
Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning—0
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning—0
A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN—0
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems—0
Automated Reinforcement Learning: An Overview—0
Adaptive Experience Selection for Policy Gradient—0
Curiosity-driven reinforcement learning with homeostatic regulation—0
Curious iLQR: Resolving Uncertainty in Model-based RL—0
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Benchmark Results

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