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 7076–7100 of 15113 papers

TitleStatusHype
Using Graph-Aware Reinforcement Learning to Identify Winning Strategies in Diplomacy Games (Student Abstract)—0
Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning—0
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning—0
Using Memory-Based Learning to Solve Tasks with State-Action Constraints—0
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response—0
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL—0
Using Part-based Representations for Explainable Deep Reinforcement Learning—0
Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks—0
Using Reinforcement Learning for Demand Response of Domestic Hot Water Buffers: a Real-Life Demonstration—0
Using Reinforcement Learning to Allocate and Manage Service Function Chains in Cellular Networks—0
Using reinforcement learning to design an AI assistantfor a satisfying co-op experience—0
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution—0
Using Reinforcement Learning to Model Incrementality in a Fast-Paced Dialogue Game—0
Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments—0
Using Reinforcement Learning to Validate Empirical Game-Theoretic Analysis: A Continuous Double Auction Study—0
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation—0
Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering—0
Continual Learning Using World Models for Pseudo-Rehearsal—0
Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation—0
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning—0
Utilization of Deep Reinforcement Learning for saccadic-based object visual search—0
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning—0
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning—0
Utilizing Skipped Frames in Action Repeats via Pseudo-Actions—0
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach—0
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Benchmark Results

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