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 4201–4210 of 15113 papers

TitleStatusHype
Adaptive patch foraging in deep reinforcement learning agents—0
Adaptive perturbation adversarial training: based on reinforcement learning—0
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning—0
Adaptive Policy Transfer in Reinforcement Learning—0
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
Adaptive Reinforcement Learning for Unobservable Random Delays—0
Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems—0
Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises—0
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Benchmark Results

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