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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4501–4525 of 15113 papers

TitleStatusHype
One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning—0
Symbolic Distillation for Learned TCP Congestion ControlCode1
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer SamplingCode0
OSS Mentor A framework for improving developers contributions via deep reinforcement learning—0
Opportunistic Episodic Reinforcement Learning—0
Understanding the Evolution of Linear Regions in Deep Reinforcement LearningCode0
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook—0
Energy Pricing in P2P Energy Systems Using Reinforcement LearningCode1
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing—0
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance—0
Hardness in Markov Decision Processes: Theory and Practice—0
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement LearningCode1
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement LearningCode0
AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience ReplayCode0
Avalon: A Benchmark for RL Generalization Using Procedurally Generated WorldsCode1
Dichotomy of Control: Separating What You Can Control from What You Cannot—0
Evaluating Long-Term Memory in 3D MazesCode1
Multi-Agent Path Finding via Tree LSTMCode1
Reachability-Aware Laplacian Representation in Reinforcement Learning—0
Climate Change Policy Exploration using Reinforcement Learning—0
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
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Benchmark Results

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