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 4231–4240 of 15113 papers

TitleStatusHype
Adaptive Trade-Offs in Off-Policy Learning—0
Adaptive trading strategies across liquidity pools—0
Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion—0
Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment—0
Adaptive Tree Backup Algorithms for Temporal-Difference Reinforcement Learning—0
Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs—0
Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx—0
Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning—0
Adapt-to-Learn: Policy Transfer in Reinforcement Learning—0
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems—0
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Benchmark Results

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