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 821–830 of 15113 papers

TitleStatusHype
On Penalty-based Bilevel Gradient Descent MethodCode1
The Wisdom of Hindsight Makes Language Models Better Instruction FollowersCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban EnvironmentsCode1
RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network ProtocolsCode1
ManiSkill2: A Unified Benchmark for Generalizable Manipulation SkillsCode1
Predictable MDP Abstraction for Unsupervised Model-Based RLCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Multi-Task Recommendations with Reinforcement LearningCode1
Mind the Gap: Offline Policy Optimization for Imperfect RewardsCode1
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Benchmark Results

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