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Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning

arXiv 1809.02121 ↗First posted 6 Sep 2018cs.LG stat.MLcited by 77

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Abstract (arXiv)

Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant. In such cases, it is sometimes easier to learn which actions not to take. In this work, we propose the Action-Elimination Deep Q-Network (AE-DQN) architecture that combines a Deep RL algorithm with an Action Elimination Network (AEN) that eliminates sub-optimal actions. The AEN is trained to predict invalid actions, supervised by an external elimination signal provided by the environment. Simulations demonstrate a considerable speedup and added robustness over vanilla DQN in text-based games with over a thousand discrete actions.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2018.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 26; 2022: 18; 2023: 8; 2024: 10; 2025: 8; 2026: 5.

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  1. 2022Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimizationcited by 244
  2. 2021DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learningcited by 65
  3. 2022Aligning to Social Norms and Values in Interactive Narrativescited by 32
  4. 2023Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agentscited by 32
  5. 2022Multi-Stage Episodic Control for Strategic Exploration in Text Gamescited by 22
  6. 2025Monte Carlo Planning with Large Language Model for Text-Based Game Agentscited by 22
  7. 2021Deep hierarchical reinforcement agents for automated penetration testingcited by 19
  8. 2021Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learningcited by 19
  9. 2021A Systematic Survey of Text Worlds as Embodied Natural Language Environmentscited by 17
  10. 2021Inherently Explainable Reinforcement Learning in Natural Languagecited by 16
  11. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspectivecited by 16
  12. 2020Benchmarking End-to-End Behavioural Cloning on Video Gamescited by 15
38 more
  1. 2024Continuous Control with Coarse-to-fine Reinforcement Learningcited by 15
  2. 2021Online Limited Memory Neural-Linear Bandits with Likelihood Matchingcited by 14
  3. 2021Training Value-Aligned Reinforcement Learning Agents Using a Normative Priorcited by 14
  4. 2021A Survey of Text Games for Reinforcement Learning informed by Natural Languagecited by 14
  5. 2021DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunitiescited by 14
  6. 2022Learning to Follow Instructions in Text-Based Gamescited by 12
  7. 2021Generalization in Text-based Games via Hierarchical Reinforcement Learningcited by 11
  8. 2021Adjacency constraint for efficient hierarchical reinforcement learningcited by 10
  9. 2021SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentationcited by 10
  10. 2022Perceiving the World: Question-guided Reinforcement Learning for Text-based Gamescited by 10
  11. 2022Behavior Cloned Transformers are Neurosymbolic Reasonerscited by 10
  12. 2023Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challengescited by 10
  13. 2021Action Redundancy in Reinforcement Learningcited by 9
  14. 2021Situated Dialogue Learning through Procedural Environment Generationcited by 9
  15. 2024Language Guided Exploration for RL Agents in Text Environmentscited by 9
  16. 2021Case-based Reasoning for Better Generalization in Textual Reinforcement Learningcited by 8
  17. 2024STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Modelscited by 8
  18. 2019Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spacescited by 7
  19. 2022DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learningcited by 7
  20. 2021SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learningcited by 6
  21. 2022Toward Causal-Aware RL: State-Wise Action-Refined Temporal Differencecited by 6
  22. 2023DanZero+: Dominating the GuanDan Game through Reinforcement Learningcited by 6
  23. 2023No Prior Mask: Eliminate Redundant Action for Deep Reinforcement Learningcited by 6
  24. 2021Planning Spatial Networks with Monte Carlo Tree Searchcited by 5
  25. 2022DanZero: Mastering GuanDan Game with Reinforcement Learningcited by 5
  26. 2024Stochastic Q-learning for Large Discrete Action Spacescited by 5
  27. 2022Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Reviewcited by 4
  28. 2023Dynamic Neighborhood Construction for Structured Large Discrete Action Spacescited by 4
  29. 2021Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learningcited by 3
  30. 2021Active Learning of Quantum System Hamiltonians yields Query Advantagecited by 3
  31. 2022Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approachcited by 3
  32. 2024Learning Uncertainty-Aware Temporally-Extended Actionscited by 3
  33. 2024EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learningcited by 3
  34. 2024Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learningcited by 3
  35. 2024BraVE: Offline Reinforcement Learning for Discrete Combinatorial Action Spacescited by 3
  36. 2021Situated Language Learning via Interactive Narrativescited by 2
  37. 2021AoI-minimizing Scheduling in UAV-relayed IoT Networkscited by 2
  38. 2021Learning Natural Language Generation from Scratchcited by 2

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