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 11101–11150 of 15113 papers

TitleStatusHype
Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making—0
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos—0
A reinforcement learning application of guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy—0
Extrapolation in Gridworld Markov-Decision Processes—0
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems—0
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions—0
Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems—0
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control—0
A non-cooperative meta-modeling game for automated third-party calibrating, validating, and falsifying constitutive laws with parallelized adversarial attacks—0
K-spin Hamiltonian for quantum-resolvable Markov decision processes—0
Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder—0
A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding—0
Kernel-Based Reinforcement Learning: A Finite-Time AnalysisCode0
Reinforcement Learning via Reasoning from Demonstration—0
Deep Reinforcement Learning for Process Control: A Primer for Beginners—0
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning—0
Self Punishment and Reward Backfill for Deep Q-LearningCode0
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels—0
Policy Gradient using Weak Derivatives for Reinforcement Learning—0
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion—0
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning—0
Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control—0
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization—0
Adaptive Stress Testing without Domain Heuristics using Go-Explore—0
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card GameCode0
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach—0
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learning—0
Solving the scalarization issues of Advantage-based Reinforcement Learning AlgorithmsCode0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation—0
Online Constrained Model-based Reinforcement Learning—0
Guided Dialog Policy Learning without Adversarial Learning in the LoopCode0
Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement LearningCode0
Intrinsic Exploration as Multi-Objective RL—0
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning—0
Uniform State Abstraction For Reinforcement Learning—0
Networked Multi-Agent Reinforcement Learning with Emergent Communication—0
Weakly-Supervised Reinforcement Learning for Controllable Behavior—0
Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy—0
Reinforced Multi-task Approach for Multi-hop Question Generation—0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing—0
Reinforcement Learning Architectures: SAC, TAC, and ESAC—0
Reinforcement Learning for Mixed-Integer Problems Based on MPC—0
Multi-agent Reinforcement Learning for Networked System Control—0
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control—0
Exploration of Reinforcement Learning for Event Camera using Car-like Robots—0
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement LearningCode0
Average Reward Adjusted Discounted Reinforcement Learning: Near-Blackwell-Optimal Policies for Real-World Applications—0
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks—0
Value Driven Representation for Human-in-the-Loop Reinforcement Learning—0
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Benchmark Results

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