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 2511–2520 of 15113 papers

TitleStatusHype
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos—0
Average-Reward Reinforcement Learning with Entropy Regularization—0
A Model-based Multi-Agent Personalized Short-Video Recommender System—0
Average-Reward Reinforcement Learning with Trust Region Methods—0
Average Reward Reinforcement Learning with Monotonic Policy Improvement—0
A model-based approach to meta-Reinforcement Learning: Transformers and tree search—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites—0
Unknown mixing times in apprenticeship and reinforcement learning—0
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives—0
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Benchmark Results

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