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 4626–4650 of 15113 papers

TitleStatusHype
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics BeliefCode0
A Mixture of Surprises for Unsupervised Reinforcement LearningCode1
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning—0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds—0
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning—0
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Real World Offline Reinforcement Learning with Realistic Data Source—0
Semi-Supervised Offline Reinforcement Learning with Action-Free TrajectoriesCode1
Reinforcement Learning with Automated Auxiliary Loss Search—0
Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningCode1
A Unified Framework for Alternating Offline Model Training and Policy LearningCode0
Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems—0
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement LearningCode0
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
The Role of Exploration for Task Transfer in Reinforcement Learning—0
Exploration via Elliptical Episodic BonusesCode1
Discovered Policy OptimisationCode3
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning—0
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios—0
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement LearningCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Multi-User Reinforcement Learning with Low Rank Rewards—0
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and PlanningCode3
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Benchmark Results

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