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

Showing 21112120 of 15113 papers

TitleStatusHype
Agent with Warm Start and Active Termination for Plane Localization in 3D UltrasoundCode1
Self-Paced Contextual Reinforcement LearningCode1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Improving Sample Efficiency in Model-Free Reinforcement Learning from ImagesCode1
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement LearningCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real TransferCode1
ModelicaGym: Applying Reinforcement Learning to Modelica ModelsCode1
Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction GuaranteesCode1
VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual NavigationCode1
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Benchmark Results

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