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 2101–2110 of 15113 papers

TitleStatusHype
Multimodal Model-Agnostic Meta-Learning via Task-Aware ModulationCode1
Learning to Manipulate Deformable Objects without DemonstrationsCode1
Learning Data Manipulation for Augmentation and WeightingCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Learning Q-network for Active Information AcquisitionCode1
Deep Reinforcement Learning Control of Quantum CartpolesCode1
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated FlightCode1
On Learning Paradigms for the Travelling Salesman ProblemCode1
Reinforcement learning with a network of spiking agentsCode1
Stabilizing Transformers for Reinforcement LearningCode1
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Benchmark Results

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