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 3476–3500 of 15113 papers

TitleStatusHype
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