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 4421–4430 of 15113 papers

TitleStatusHype
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning—0
A Generalised Inverse Reinforcement Learning Framework—0
A Generalized Natural Actor-Critic Algorithm—0
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning—0
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing—0
A General Perspective on Objectives of Reinforcement Learning—0
A General Theory of Relativity in Reinforcement Learning—0
A Generative Framework for Simultaneous Machine Translation—0
Agent-Agnostic Human-in-the-Loop Reinforcement Learning—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
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Benchmark Results

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