mlsanityData to 14 Oct 2026

Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid

arXiv 2404.01794 ↗First posted 2 Apr 2024cs.AIcited by 1

On the map: Offline RL and imitation learning from data → Decision transformers and sequence modeling

also in 1 more
Abstract (arXiv)

Autonomous and learning systems based on Deep Reinforcement Learning have firmly established themselves as a foundation for approaches to creating resilient and efficient Cyber-Physical Energy Systems. However, most current approaches suffer from two distinct problems: Modern model-free algorithms such as Soft Actor Critic need a high number of samples to learn a meaningful policy, as well as a fallback to ward against concept drifts (e. g., catastrophic forgetting). In this paper, we present the work in progress towards a hybrid agent architecture that combines model-based Deep Reinforcement Learning with imitation learning to overcome both problems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data. Builds on 21 of its 43 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

21 of 43 references.

References · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  6. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  7. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  8. 2020Towards Continual Reinforcement Learning: A Review and Perspectivescited by 274
  9. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 231
  10. 2019Benchmarking Model-Based Reinforcement Learningcited by 162
  11. 2024Robust agents learn causal world modelscited by 81
  12. 2022A Survey on Model-based Reinforcement Learningcited by 66
9 more
  1. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 50
  2. 2021Stability Constrained Reinforcement Learning for Real-Time Voltage Controlcited by 15
  3. 2019Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learningcited by 8
  4. 2018A Learning-based Power Management for Networked Microgrids Under Incomplete Informationcited by 6
  5. 2020Combining Model-Based and Model-Free Methods for Nonlinear Control: A Provably Convergent Policy Gradient Approachcited by 5
  6. 2021A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systemscited by 5
  7. 2018Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systemscited by 3
  8. 2020Deep Reinforcement Learning for Electric Transmission Voltage Controlcited by 3
  9. 2023ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agentscited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024On zero-shot learning in neural state estimation of power distribution systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025An Introduction to Deep Reinforcement and Imitation Learning
  2. 2020Rethink AI-based Power Grid Control: Diving Into Algorithm Design
  3. 2024Imitation Learning for Intra-Day Power Grid Operation through Topology Actions
  4. 2018Imitation Learning with Concurrent Actions in 3D Games
  5. 2020Energy-Based Imitation Learning
  6. 2019Hybrid Reinforcement Learning with Expert State Sequences
  7. 2018Multi-Agent Generative Adversarial Imitation Learning
  8. 2020Adversarial Imitation Learning via Random Search