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 3731–3740 of 15113 papers

TitleStatusHype
Corruption-robust exploration in episodic reinforcement learning—0
A stabilizing reinforcement learning approach for sampled systems with partially unknown models—0
Automated Video Game Testing Using Synthetic and Human-Like Agents—0
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks—0
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning—0
Adaptive patch foraging in deep reinforcement learning agents—0
Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards—0
Deep Reinforcement Learning for System-on-Chip: Myths and Realities—0
A Meta-Reinforcement Learning Approach to Process Control—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
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Benchmark Results

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