Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments (2506.06981v1)

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.

• The paper reveals that model-free DRL agents exhibit structured exploration and implicit planning without explicit memory or world models.
• The study employs neuroscience-inspired decoding techniques to uncover allocentric spatial encoding in agents’ internal states.
• The research demonstrates that integrating neuroethological methods in deep RL analysis enhances our understanding of agent behavior in realistic, complex settings.

Deep Reinforcement Learning: Behavioral Analysis in Complex Environments

The paper "Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments" underscores the importance of advanced behavioral analytical tools to understand the strategies of Deep Reinforcement Learning (DRL) agents. Standard reward curve comparisons are insufficient to capture the complexity of behaviors emerging in sophisticated tasks. The authors leverage methodologies from neuroscience and ethology to dissect the decision-making processes of DRL agents within the novel environment, ForageWorld.

ForageWorld is a partially observable, procedurally generated platform that mimics real-world ecological scenarios. It includes elements such as sparse resources, predator threats, and expansive arenas, aligning with the natural foraging challenges faced by animals. The researchers aim to explore whether model-free RNN-based agents can exhibit planning behaviors without explicit memory modules or world models, challenging the traditional assumption that such capabilities are exclusive to model-based systems.

Key findings reveal that DRL agents display structured exploration and revisitation patterns analogous to animal foraging behaviors. These agents leverage memory-derived knowledge to optimize their navigation and resource utilization strategies. Through the use of neuroscience-inspired decoding techniques, the paper uncovers that agents encode allocentric spatial information in their internal states, allowing for implicit memory usage and planning.

Quantitative analysis further illustrates that factors such as positional uncertainty, prior predator encounters, and resource patch quality influence the foraging decisions made by agents. Notably, the paper demonstrates that sparsity in network architectures enhances both the efficiency of information encoding and the interpretability of agent behaviors.

The implications of this research are multifaceted, offering insights into the intersection of artificial and biological intelligence. Practically, the findings suggest that incorporating neuroethological methods into AI analysis can unveil emergent capabilities within DRL systems, which may otherwise be overlooked. Theoretically, the paper posits that bridging tools from neuroscience to AI is crucial for ensuring safe alignment and fostering desirable behaviors in increasingly autonomous agents.

Moving forward, this research prompts the exploration of cognitive map formations and recurrent architectures that parallel mammalian spatial navigation capabilities. By integrating grid-based representations and assessing their influence in complex tasks, future work may enhance the interpretability and memory-planning abilities of DRL agents. Additionally, ForageWorld serves as a robust platform for developing curriculum-based training frameworks, potentially enriching the developmental trajectory of artificial cognition systems.

In summary, effective DRL behavioral analysis necessitates a comprehensive framework that captures the intricate dynamics of learning, memory, and decision-making. This paper provides a pivotal step towards understanding model-free agent competences, with profound implications for the development and governance of advanced AI systems.