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

2025-06-08

AI Summary

Deep reinforcement learning (DRL) agents’ behavioral mechanisms in complex, open-ended environments remain poorly understood, particularly their implicit planning capabilities—such as resource prediction, risk avoidance, and path optimization—in the absence of explicit memory or world models. Method: Leveraging the ForageWorld environment, we integrate neuroscience-inspired and behavioral-ecology methodologies to systematically analyze model-free recurrent neural network (RNN) agents. We propose the first neuro-behavioral joint analysis framework for DRL, unifying trajectory quantification, interpretable latent-state analysis, and cross-scale representation–behavior association modeling. Results: Our framework reveals that implicit planning emerges dynamically from RNN dynamics, significantly enhancing behavioral interpretability and policy alignment under sparse rewards, dynamic threats, and spatially extended settings. It generalizes robustly across diverse tasks, establishing a principled bridge between neuroethology and artificial intelligence.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Intelligent Robots: Behavior Learning & Control

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Analyzing behavior of deep RL agents in complex environments
• Exploring implicit planning in model-free agents without memory
• Developing neuroethology-inspired tools for agent behavior analysis

Innovation

• Applying neuroethology tools to DRL analysis
• Model-free agents show emergent planning behavior
• General framework linking behavior to neural dynamics