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 2471–2480 of 15113 papers

TitleStatusHype
Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning—0
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning—0
PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learning—0
Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches—0
Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach—0
A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem—0
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
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Benchmark Results

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