Prior preferences in active inference agents: soft, hard, and goal shaping

Active inference, rooted in Friston’s free energy principle, models cognition as Bayesian inference minimizing prediction error. Early models focused on perception, but recent developments incorporate decision-making via expected free energy (EFE). Prior works like Bayesian surprise, variational inference, and hierarchical generative models have advanced the field, enabling applications in robotics and neuroscience. However, the role of explicit preference encoding—how goals are represented and influence behavior—remains underexplored. Traditional reinforcement learning emphasizes reward signals, but active inference uses prior preferences, offering more flexible goal specification. Despite progress, the impact of preference configurations on exploration-exploitation trade-offs and learning dynamics is still unclear, motivating this systematic analysis.

The core challenge is understanding how different preference distributions—hard or soft goals, with or without goal shaping—affect the behavior and learning efficiency of active inference agents. Specifically, how these configurations influence the calculation of EFE, the balance between exploration and exploitation, and the agent’s ability to learn environmental transition dynamics. Existing models often assume fixed or implicit preferences, lacking a systematic comparison of explicit preference strategies. This gap limits the design of adaptive agents capable of flexible goal pursuit in uncertain environments, which is critical for real-world applications like autonomous navigation and decision support systems.

The paper introduces a comprehensive framework for defining and comparing four preference distribution strategies: hard goals with and without goal shaping, and soft goals with and without goal shaping. It innovatively integrates these strategies into the variational Bayesian active inference model, explicitly analyzing their impact on EFE and policy selection. The core innovation lies in formalizing the influence of goal strength and temporal shaping on exploration-exploitation balance, providing a systematic methodology for preference tuning. This approach bridges the gap between theoretical models and practical agent design, enabling more flexible and goal-oriented autonomous systems.

• �� Model the environment as a discrete POMDP with states, observations, actions, and parameters.
• �� Define four preference distributions varying in goal strength (hard/soft) and temporal shaping (with/without).
• �� Use variational Bayesian inference to minimize VFE, updating beliefs about states, policies, and model parameters.
• �� Calculate EFE incorporating KL divergence between variational beliefs and preferences, balancing risk and epistemic value.
• �� Implement policy selection via Bayesian model averaging, choosing actions that minimize expected free energy.
• �� Conduct experiments in a 3x3 grid environment, measuring success rate, exploration, and learning speed across preference configurations.

Agents trained in a grid world with predefined start and goal states, under four preference schemes. Metrics include task success rate, exploration extent, and convergence speed. Multiple runs ensure statistical robustness. The experiment tests how preference strength and goal shaping influence performance, with additional ablation to analyze the contribution of each component. Results are compared against baseline models without explicit preferences, validating the impact of goal configuration on behavior and learning dynamics.

Goal shaping significantly boosts task success (up to 85%) but reduces exploration (~30%), confirming its exploitation bias. Hard goals accelerate convergence but impair adaptability, while soft goals foster exploration at the cost of slower task completion. Preference shaping effectively guides agents toward desired outcomes, yet limits environmental model learning. These findings demonstrate that preference design critically influences exploration-exploitation trade-offs, providing actionable insights for autonomous system tuning.

The insights inform autonomous navigation, robotic control, and decision support systems requiring flexible goal setting. By tuning preference configurations, designers can optimize agents for specific tasks—prioritizing rapid goal achievement or environmental understanding. The framework can be integrated with deep learning for scalable, real-time applications. Long-term, it supports development of adaptive agents capable of dynamic goal adjustment, enhancing robustness in unpredictable environments.

The study is limited to low-dimensional grid environments; scalability to high-dimensional or continuous spaces remains untested. Preferences are predefined and static, lacking adaptive mechanisms. Computational complexity of inference and policy optimization poses challenges for real-time deployment. Future work should address these issues, exploring adaptive preference learning, scalable algorithms, and broader environment applicability.