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 4521–4530 of 15113 papers

TitleStatusHype
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning—0
LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation—0
A Cooperative Reinforcement Learning Environment for Detecting and Penalizing Betrayal—0
Learning General World Models in a Handful of Reward-Free Deployments—0
MetaEMS: A Meta Reinforcement Learning-based Control Framework for Building Energy Management System—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES—0
Probing Transfer in Deep Reinforcement Learning without Task Engineering—0
Towards Quantum-Enabled 6G Slicing—0
Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR StreamingCode0
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Benchmark Results

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