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 4331–4340 of 15113 papers

TitleStatusHype
Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot—0
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and TrainabilityCode0
Solving Offline Reinforcement Learning with Decision Tree RegressionCode0
Large-scale Reinforcement Learning for Diffusion Models—0
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough ReproductionCode0
Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation—0
DeLF: Designing Learning Environments with Foundation ModelsCode0
Deployable Reinforcement Learning with Variable Control RateCode0
Blackout Mitigation via Physics-guided RLCode0
Crowd-PrefRL: Preference-Based Reward Learning from Crowds—0
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Benchmark Results

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