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

Showing 1130111350 of 15113 papers

TitleStatusHype
Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions0
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning0
Using Cyber Terrain in Reinforcement Learning for Penetration Testing0
Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms0
Using Deep Reinforcement Learning for Zero Defect Smart Forging0
Using Deep Reinforcement Learning to Enhance Channel Sampling Patterns in Integrated Sensing and Communication0
Using Deep Reinforcement Learning to Generate Rationales for Molecules0
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem0
Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures0
Using Enhanced Gaussian Cross-Entropy in Imitation Learning to Digging the First Diamond in Minecraft0
Using Experience Classification for Training Non-Markovian Tasks0
Using General Value Functions to Learn Domain-Backed Inventory Management Policies0
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 Planning0
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning0
Using Memory-Based Learning to Solve Tasks with State-Action Constraints0
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response0
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL0
Using Part-based Representations for Explainable Deep Reinforcement Learning0
Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks0
Using Reinforcement Learning for Demand Response of Domestic Hot Water Buffers: a Real-Life Demonstration0
Using Reinforcement Learning to Allocate and Manage Service Function Chains in Cellular Networks0
Using reinforcement learning to design an AI assistantfor a satisfying co-op experience0
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution0
Using Reinforcement Learning to Model Incrementality in a Fast-Paced Dialogue Game0
Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments0
Using Reinforcement Learning to Validate Empirical Game-Theoretic Analysis: A Continuous Double Auction Study0
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation0
Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering0
Continual Learning Using World Models for Pseudo-Rehearsal0
Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation0
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning0
Utilization of Deep Reinforcement Learning for saccadic-based object visual search0
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning0
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning0
Utilizing Skipped Frames in Action Repeats via Pseudo-Actions0
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach0
V2N Service Scaling with Deep Reinforcement Learning0
VacciNet: Towards a Smart Framework for Learning the Distribution Chain Optimization of Vaccines for a Pandemic0
Vairiational Stochastic Games0
Validation of massively-parallel adaptive testing using dynamic control matching0
Value-Added Chemical Discovery Using Reinforcement Learning0
Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning0
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems0
Bayesian Meta-reinforcement Learning for Traffic Signal Control0
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings0
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning0
Value-driven Hindsight Modelling0
Value Driven Representation for Human-in-the-Loop Reinforcement Learning0
Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization0
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Benchmark Results

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