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 8801–8825 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module—0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search—0
Representation Matters: Offline Pretraining for Sequential Decision Making—0
Multi-Task Reinforcement Learning with Context-based RepresentationsCode1
Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users—0
Leveraging Reinforcement Learning for evaluating Robustness of KNN Search Algorithms—0
Domain Adaptation In Reinforcement Learning Via Latent Unified State RepresentationCode1
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-SupervisionCode1
Derivative-Free Reinforcement Learning: A Review—0
Learning Equational Theorem Proving—0
Defense Against Reward Poisoning Attacks in Reinforcement Learning—0
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning—0
Personalization for Web-based Services using Offline Reinforcement Learning—0
Policy Augmentation: An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning AlgorithmsCode0
Risk-Averse Offline Reinforcement LearningCode1
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States—0
Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communications—0
Patterns, predictions, and actions: A story about machine learning—0
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach—0
Risk-Averse Bayes-Adaptive Reinforcement Learning—0
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning—0
Adaptive Pairwise Weights for Temporal Credit Assignment—0
Measuring Progress in Deep Reinforcement Learning Sample Efficiency—0
rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching TasksCode1
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Benchmark Results

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