mlsanityData to 7 Oct 2026

An Introduction to Deep Reinforcement and Imitation Learning

arXiv 2512.08052 ↗First posted 8 Dec 2025cs.RO cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Embodied agents, such as robots and virtual characters, must continuously select actions to execute tasks effectively, solving complex sequential decision-making problems. Given the difficulty of designing such controllers manually, learning-based approaches have emerged as promising alternatives, most notably Deep Reinforcement Learning (DRL) and Deep Imitation Learning (DIL). DRL leverages reward signals to optimize behavior, while DIL uses expert demonstrations to guide learning. This document introduces DRL and DIL in the context of embodied agents, adopting a concise, depth-first approach to the literature. It is self-contained, presenting all necessary mathematical and machine learning concepts as they are needed. It is not intended as a survey of the field; rather, it focuses on a small set of foundational algorithms and techniques, prioritizing in-depth understanding over broad coverage. The material ranges from Markov Decision Processes to REINFORCE and Proximal Policy Optimization (PPO) for DRL, and from Behavioral Cloning to Dataset Aggregation (DAgger) and Generative Adversarial Imitation Learning (GAIL) for DIL.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 7 of its 23 references that are in the map. First posted on arXiv 8 Dec 2025.

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7 of 23 references.

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  1. 2022imitation: Clean Imitation Learning Implementations22 Nov

Other references · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  5. 2016End to End Learning for Self-Driving Carscited by 1,000
  6. 2018Unity: A General Platform for Intelligent Agentscited by 232

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  1. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
  2. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
  3. 2018An Algorithmic Perspective on Imitation Learning
  4. 2021Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning
  5. 2024The Duality of Generative AI and Reinforcement Learning in Robotics: A Review
  6. 2018Imitation Learning with Concurrent Actions in 3D Games
  7. 2024A Dual Approach to Imitation Learning from Observations with Offline Datasets
  8. 2025Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning