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 8151–8200 of 15113 papers

TitleStatusHype
Parameter Sharing Reinforcement Learning Architecture for Multi Agent Driving Behaviors—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
Paraphrase Generation with Deep Reinforcement Learning—0
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation—0
Parenting: Safe Reinforcement Learning from Human Input—0
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks—0
Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP—0
Pareto Policy Adaptation—0
Pareto Policy Pool for Model-based Offline Reinforcement Learning—0
Pareto Set Learning for Multi-Objective Reinforcement Learning—0
ParMod: A Parallel and Modular Framework for Learning Non-Markovian Tasks—0
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning—0
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation—0
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning—0
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning—0
Part-Activated Deep Reinforcement Learning for Action Prediction—0
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing—0
Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach—0
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms—0
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning—0
Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images—0
Partial Simulation for Imitation Learning—0
Particle Based Stochastic Policy Optimization—0
Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies—0
Particle Value Functions—0
Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks—0
Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains—0
Partner Personas Generation for Dialogue Response Generation—0
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning—0
Passing Through Narrow Gaps with Deep Reinforcement Learning—0
Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems—0
Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots—0
Pathfinding in Random Partially Observable Environments with Vision-Informed Deep Reinforcement Learning—0
Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach—0
Path Integral Networks: End-to-End Differentiable Optimal Control—0
Machine learning strategies for path-planning microswimmers in turbulent flows—0
Path Planning of Cleaning Robot with Reinforcement Learning—0
Path Planning using Reinforcement Learning: A Policy Iteration Approach—0
Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer—0
Patterns, predictions, and actions: A story about machine learning—0
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching—0
Pauli Network Circuit Synthesis with Reinforcement Learning—0
Paused Agent Replay Refresh—0
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making—0
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning—0
PDQN - A Deep Reinforcement Learning Method for Planning with Long Delays: Optimization of Manufacturing Dispatching—0
PEARL: Parallelized Expert-Assisted Reinforcement Learning for Scene Rearrangement Planning—0
PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning—0
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Benchmark Results

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