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 11226–11250 of 15113 papers

TitleStatusHype
GST: Group-Sparse Training for Accelerating Deep Reinforcement Learning—0
Guaranteed satisficing and finite regret: Analysis of a cognitive satisficing value function—0
Guaranteed Trust Region Optimization via Two-Phase KL Penalization—0
Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation—0
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation—0
Guarded Policy Optimization with Imperfect Online Demonstrations—0
"Guess what I'm doing": Extending legibility to sequential decision tasks—0
Guided by Guardrails: Control Barrier Functions as Safety Instructors for Robotic Learning—0
Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion—0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Guiding Robot Exploration in Reinforcement Learning via Automated Planning—0
Guided Exploration in Deep Reinforcement Learning—0
Guided Meta-Policy Search—0
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration—0
Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process—0
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation—0
Guided Safe Shooting: model based reinforcement learning with safety constraints—0
LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation—0
Guiding Global Placement With Reinforcement Learning—0
Guiding Reinforcement Learning Exploration Using Natural Language—0
Guiding Representation Learning in Deep Generative Models with Policy Gradients—0
Guiding Safe Exploration with Weakest Preconditions—0
Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education—0
gym-DSSAT: a crop model turned into a Reinforcement Learning environment—0
Gym-preCICE: Reinforcement Learning Environments for Active Flow Control—0
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Benchmark Results

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