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 3451–3500 of 15113 papers

TitleStatusHype
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance—0
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Deep Residual Reinforcement Learning—0
Auto-Encoding Adversarial Imitation Learning—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Auto-Encoding Inverse Reinforcement Learning—0
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities—0
Dialogue Shaping: Empowering Agents through NPC Interaction—0
A Learning Framework for High Precision Industrial Assembly—0
Deep Occupancy-Predictive Representations for Autonomous Driving—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning—0
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation—0
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning—0
Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting—0
DeepPlace: Learning to Place Applications in Multi-Tenant Clusters—0
A Structure-aware Online Learning Algorithm for Markov Decision Processes—0
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning—0
DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning—0
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality—0
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition—0
Automata-Guided Hierarchical Reinforcement Learning for Skill Composition—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION—0
Counterfactual Explanation Policies in RL—0
Automata Guided Reinforcement Learning With Demonstrations—0
Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market—0
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Deep Q-Network for AI Soccer—0
A Strong Baseline for Batch Imitation Learning—0
Counterfactual Credit Assignment in Model-Free Reinforcement Learning—0
A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow—0
Agent based modelling for continuously varying supply chains—0
Automated Database Indexing using Model-free Reinforcement Learning—0
DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning—0
Deep Randomized Least Squares Value Iteration—0
Deep Radial-Basis Value Functions for Continuous Control—0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
Deep reinforced active learning for multi-class image classification—0
Deep Reinforced Self-Attention Masks for Abstractive Summarization (DR.SAS)—0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Deep Reinforcement Fuzzing—0
Deep RL With Information Constrained Policies: Generalization in Continuous Control—0
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Benchmark Results

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