mlsanityData to 14 Oct 2026

Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments

arXiv 2506.06981 ↗First posted 8 Jun 2025cs.AI cs.LGcited by 4

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Understanding the behavior of deep reinforcement learning (DRL) agents -particularly as task and agent sophistication increase- requires more than simple comparison of reward curves, yet standard methods for behavioral analysis remain underdeveloped in DRL. We apply tools from neuroscience and ethology to study DRL agents in a novel, complex, partially observable environment, ForageWorld, designed to capture key aspects of real-world animal foraging- including sparse, depleting resource patches, predator threats, and spatially extended arenas. We use this environment as a platform for applying joint behavioral and neural analysis to agents, revealing detailed, quantitatively grounded insights into agent strategies, memory, and planning. Contrary to common assumptions, we find that model-free RNN-based DRL agents can exhibit structured, planning-like behavior purely through emergent dynamics- without requiring explicit memory modules or world models. Our results show that studying DRL agents like animals -analyzing them with neuroethology-inspired tools that reveal structure in both behavior and neural dynamics- uncovers rich structure in their learning dynamics that would otherwise remain invisible. We distill these tools into a general analysis framework linking core behavioral and representational features to diagnostic methods, which can be reused for a wide range of tasks and agents. As agents grow more complex and autonomous, bridging neuroscience, cognitive science, and AI will be essential- not just for understanding their behavior, but for ensuring safe alignment and maximizing desirable behaviors that are hard to measure via reward. We show how this can be done by drawing on lessons from how biological intelligence is studied.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 48 of its 118 references that are in the map. First posted on arXiv 8 Jun 2025.

Builds on

48 of 118 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Predicting Future Actions of Reinforcement Learning Agents29 Oct
  2. 2024Simplifying Deep Temporal Difference Learning5 Jul

Other references · 46

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  3. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,262
  4. 2019Are Sixteen Heads Really Better than One?cited by 816
  5. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 478
  6. 2018Quantifying Generalization in Reinforcement Learningcited by 352
  7. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 327
  8. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  9. 2024Reinforcement learningcited by 247
  10. 2018Programmatically Interpretable Reinforcement Learningcited by 193
  11. 2021Open-Ended Learning Leads to Generally Capable Agentscited by 182
  12. 2018Assessing Generalization in Deep Reinforcement Learningcited by 133
34 more
  1. 2021Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPscited by 122
  2. 2022Learnable latent embeddings for joint behavioral and neural analysiscited by 117
  3. 2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learningcited by 88
  4. 2023Evaluating Cognitive Maps and Planning in Large Language Models with CogEvalcited by 77
  5. 2021Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robotscited by 76
  6. 2021Relating transformers to models and neural representations of the hippocampal formationcited by 62
  7. 2024Open-Nav: Exploring Zero-Shot Vision-and-Language Navigation in Continuous Environment with Open-Source LLMscited by 62
  8. 2019Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Researchcited by 61
  9. 2023XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAXcited by 55
  10. 2018Scale-free networks are rarecited by 40
  11. 2023Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Predictioncited by 39
  12. 2023Skill Reinforcement Learning and Planning for Open-World Long-Horizon Taskscited by 36
  13. 2023Don't trust your eyes: on the (un)reliability of feature visualizationscited by 32
  14. 2023MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learningcited by 31
  15. 2022Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learningcited by 28
  16. 2022Investigating the Properties of Neural Network Representations in Reinforcement Learningcited by 24
  17. 2023Creative Agents: Empowering Agents with Imagination for Creative Taskscited by 24
  18. 2023Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysiscited by 23
  19. 2024A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoalscited by 23
  20. 2022General Intelligence Requires Rethinking Explorationcited by 20
  21. 2022Deconfounded Representation Similarity for Comparison of Neural Networkscited by 19
  22. 2025Interpreting Emergent Planning in Model-Free Reinforcement Learningcited by 15
  23. 2024Goals as Reward-Producing Programscited by 14
  24. 2023JaxPruner: A concise library for sparsity researchcited by 13
  25. 2023Thinker: Learning to Plan and Actcited by 13
  26. 2025Improving Transformer World Models for Data-Efficient RLcited by 13
  27. 2025A Survey on Explainable Deep Reinforcement Learningcited by 12
  28. 2023minimax: Efficient Baselines for Autocurricula in JAXcited by 10
  29. 2025Better artificial intelligence does not mean better models of biologycited by 10
  30. 2024Predictive representations: building blocks of intelligencecited by 4
  31. 2024Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agentcited by 4
  32. 2023Open-ended search for environments and adapted agents using MAP-Elitescited by 2
  33. 2023Grid Cell-Inspired Fragmentation and Recall for Efficient Map Buildingcited by 2
  34. 2023Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Controlcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RLcited by 5
  2. 2025Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rulescited by 4
  3. 2025Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agentscited by 0
  4. 2026Forager: a lightweight testbed for continual learning with partial observability in RLcited by 0

Similar, not cited

Close in wording, no citation either way

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  3. 2025Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents
  4. 2026Task-Induced Representational Invariances Depend on Learning Objective in Deep RL
  5. 2025An Introduction to Deep Reinforcement and Imitation Learning
  6. 2026Sample-Efficient Neurosymbolic Deep Reinforcement Learning
  7. 2024Deep reinforcement learning with time-scale invariant memory
  8. 2026FARL: A Fully Automated Pipeline of RL Interface and Solver Design