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 3901–3910 of 15113 papers

TitleStatusHype
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control—0
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback—0
Distributional Decision Transformer for Hindsight Information Matching—0
Distributionally Robust Imitation Learning—0
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning—0
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning—0
Optimism and Delays in Episodic Reinforcement Learning—0
Delayed Geometric Discounts: An Alternative Criterion for Reinforcement Learning—0
Delayed Reinforcement Learning by Imitation—0
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization—0
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Benchmark Results

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