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 8901–8950 of 15113 papers

TitleStatusHype
Multi-agent Battery Storage Management using MPC-based Reinforcement Learning—0
Towards robust and domain agnostic reinforcement learning competitions—0
XIRL: Cross-embodiment Inverse Reinforcement LearningCode0
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces—0
Correcting Momentum in Temporal Difference LearningCode0
Learning to Guide a Saturation-Based Theorem Prover—0
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning—0
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems—0
Average-Reward Reinforcement Learning with Trust Region Methods—0
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint—0
Learning Combinatorial Node Labeling Algorithms—0
Entropy Regularized Reinforcement Learning Using Large Deviation TheoryCode0
Identifiability in inverse reinforcement learning—0
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning—0
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning—0
Heuristic-Guided Reinforcement Learning—0
Learning Routines for Effective Off-Policy Reinforcement Learning—0
Reinforcement Learning for Assignment Problem with Time Constraints—0
Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learning—0
Robustifying Reinforcement Learning Policies with L_1 Adaptive Control—0
Detecting and Adapting to Novelty in Games—0
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RLCode0
Be Considerate: Objectives, Side Effects, and Deciding How to Act—0
Hyperbolically-Discounted Reinforcement Learning on Reward-Punishment Framework—0
Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning—0
Grounding Complex Navigational Instructions Using Scene Graphs—0
LiMIIRL: Lightweight Multiple-Intent Inverse Reinforcement Learning—0
MICo: Improved representations via sampling-based state similarity for Markov decision processesCode0
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement LearningCode0
Safe RAN control: A Symbolic Reinforcement Learning Approach—0
Towards Learning to Play Piano with Dexterous Hands and Touch—0
Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour—0
Towards Deeper Deep Reinforcement Learning with Spectral Normalization—0
Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning—0
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making—0
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning—0
Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes—0
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning—0
An Entropy Regularization Free Mechanism for Policy-based Reinforcement Learning—0
A Coarse to Fine Question Answering System based on Reinforcement Learning—0
Ad Headline Generation using Self-Critical Masked Language Model—0
Quantitative Day Trading from Natural Language using Reinforcement Learning—0
Reward is enough for convex MDPs—0
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs—0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding—0
Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning—0
Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks—0
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Benchmark Results

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