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 2011–2020 of 15113 papers

TitleStatusHype
DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment RegimeCode1
LeDex: Training LLMs to Better Self-Debug and Explain Code—0
Reinforcement Learning in Dynamic Treatment Regimes Needs Critical ReexaminationCode1
Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RLCode1
Highway Reinforcement Learning—0
Mollification Effects of Policy Gradient Methods—0
Safe Reinforcement Learning in Black-Box Environments via Adaptive ShieldingCode0
Extreme Value Monte Carlo Tree Search—0
Large Language Model-Driven Curriculum Design for Mobile NetworksCode0
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective—0
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Benchmark Results

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