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 1681–1690 of 15113 papers

TitleStatusHype
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic EnvironmentsCode1
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural NetworksCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
Eigenoption Discovery through the Deep Successor RepresentationCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
PaCo: Parameter-Compositional Multi-Task Reinforcement LearningCode1
Embodied Synaptic Plasticity with Online Reinforcement learningCode1
Emergence of Locomotion Behaviours in Rich EnvironmentsCode1
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand SystemsCode1
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for RoboticsCode1
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Benchmark Results

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