Data to 14 Oct 2026

Deep Reinforcement Learning in Parameterized Action Space

arXiv 1511.04143 ↗First posted 13 Nov 2015cs.AI cs.LG cs.MAcited by 68

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Abstract (arXiv)

Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, to the best of our knowledge no previous work has succeeded at using deep neural networks in structured (parameterized) continuous action spaces. To fill this gap, this paper focuses on learning within the domain of simulated RoboCup soccer, which features a small set of discrete action types, each of which is parameterized with continuous variables. The best learned agent can score goals more reliably than the 2012 RoboCup champion agent. As such, this paper represents a successful extension of deep reinforcement learning to the class of parameterized action space MDPs.

Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Nov 2015.

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Citing papers per year: 2020: 1; 2021: 19; 2022: 12; 2023: 8; 2024: 8; 2025: 9; 2026: 11.

68 papers.

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2024Behavior Generation with Latent Actionscited by 160
2021From Motor Control to Team Play in Simulated Humanoid Footballcited by 87
2021Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Taskscited by 78
2025VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizerscited by 70
2021Accelerating Robotic Reinforcement Learning via Parameterized Action Primitivescited by 65
2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
2021Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domaincited by 58
2022VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learningcited by 52
2020Deep Imitation Learning for Bimanual Robotic Manipulationcited by 50
2021Adversarial Intrinsic Motivation for Reinforcement Learningcited by 40
2021HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representationcited by 35
2021Towards Distraction-Robust Active Visual Trackingcited by 30

38 more

2023HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulationcited by 29
2021Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learningcited by 27
2021Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuningcited by 24
2024Practice Makes Perfect: Planning to Learn Skill Parameter Policiescited by 23
2023LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarioscited by 20
2025Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulationcited by 14
2021Reliably-stabilizing piecewise-affine neural network controllerscited by 13
2021Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learningcited by 10
2024PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learningcited by 8
2024Model-based Reinforcement Learning for Parameterized Action Spacescited by 8
2022Transfering Hierarchical Structure with Dual Meta Imitation Learningcited by 7
2022Meta-Learning Parameterized Skillscited by 7
2022ASPiRe:Adaptive Skill Priors for Reinforcement Learningcited by 7
2023Deep Reinforcement Learning for Autonomous Cyber Defence: A Surveycited by 7
2023Parameterized Decision-making with Multi-modal Perception for Autonomous Drivingcited by 7
2024DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learningcited by 6
2024Discretionary Lane-Change Decision and Control via Parameterized Soft Actor-Critic for Hybrid Action Spacecited by 6
2021One model Packs Thousands of Items with Recurrent Conditional Query Learningcited by 5
2022Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree searchcited by 5
2021Design Strategy Network: A deep hierarchical framework to represent generative design strategies in complex action spacescited by 4
2021Generalizing Object-Centric Task-Axes Controllers using Keypointscited by 3
2022Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templatescited by 2
2022On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfercited by 2
2023Efficient Learning of High Level Plans from Playcited by 2
2026PlatoLTL: Scaling LTL-Guided Multi-Task RLcited by 2
2021Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination through Curriculum-Learning and Competitioncited by 1
2022Robust stabilization of polytopic systems via fast and reliable neural network-based approximationscited by 1
2023Diverse Policy Optimization for Structured Action Spacecited by 1
2023Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learningcited by 1
2024Constrained Optimal Fuel Consumption of HEV: A Constrained Reinforcement Learning Approachcited by 1
2025Towards Learning Scalable Agile Dynamic Motion Planning for Robosoccer Teams with Policy Optimizationcited by 1
2026Hybrid TD3: Overestimation Bias Analysis and Stable Policy Optimization for Hybrid Action Spacecited by 1
2021Deep Reinforcement Learning for Backup Strategies against Adversariescited by 0
2021Curious Exploration and Return-based Memory Restoration for Deep Reinforcement Learningcited by 0
2021An Anytime Hierarchical Approach for Stochastic Task and Motion Planningcited by 0
2022Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learningcited by 0
2022Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropycited by 0
2022A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Spacecited by 0

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