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 4071–4080 of 15113 papers

TitleStatusHype
Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems—0
Action-Gap Phenomenon in Reinforcement Learning—0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning—0
Action Guidance with MCTS for Deep Reinforcement Learning—0
Action-modulated midbrain dopamine activity arises from distributed control policies—0
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance—0
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning—0
Action Redundancy in Reinforcement Learning—0
Action Set Based Policy Optimization for Safe Power Grid Management—0
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos—0
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Benchmark Results

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