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 951–960 of 15113 papers

TitleStatusHype
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Hearts Gym: Learning Reinforcement Learning as a Team EventCode1
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic ControlCode1
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational GraphCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
Hierarchical Kickstarting for Skill Transfer in Reinforcement LearningCode1
Hierarchical Reinforcement Learning By Discovering Intrinsic OptionsCode1
A Modular Framework for Reinforcement Learning Optimal ExecutionCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
Discrete Codebook World Models for Continuous ControlCode1
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Benchmark Results

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