Self-Predictive Agents

• Self-predictive agents are artificial systems that use internal models to anticipate future states and prediction errors, driving autonomous exploration and adaptation.
• They integrate world-model and self-model architectures to predict the consequences of actions, which guides intrinsic motivation and supports robust representation learning.
• Adaptive goal mechanisms, bidirectional prediction, and symbolic frameworks enable these agents to transfer skills and coordinate effectively in complex multi-agent environments.

Self-predictive agents are artificial systems endowed with mechanisms to anticipate their own future states, actions, and—often critically—the errors or uncertainties in such predictions. This capability is typically realized through internal models, self-supervised objectives, or explicit prediction-error tracking, leading to autonomous behavior adaptation, improved exploration, and transferable knowledge representations. The self-predictive paradigm spans deep reinforcement learning (RL), cognitive architectures, world modeling, and LLM agents, with instantiations ranging from classic world/self-model frameworks to modern action-conditional representation learning and symbolic optimization in agentic pipelines.

1. Intrinsic Motivation and Predictive World-Model Architectures

Self-predictive agents rely on architectures that integrate both forward-predictive and error-monitoring models to drive exploratory and adaptive behaviors.

• The canonical approach involves two components: a world-model that predicts the consequences of actions based on raw sensory history, and a self-model that forecasts the future prediction error of the world-model. The interplay between these systems is formalized by summarizing the predicted error using an expectation function,

$$\sigma(a)[o_{t-1}:t] = \sum_i \sum_{c \in C} c \cdot p_i\bigl(c \mid o_{t-1}:t, a\bigr)$$

Actions are sampled according to a Boltzmann distribution that favors states with high anticipated error (i.e., those that are maximally surprising to the agent’s world-model).

• The predictive modeling can take several forms, notably: inverse dynamics prediction (learning appropriate actions given past/future observations) or latent-space forward prediction (progressing compressive encodings through time).
• This intrinsic motivation structure operationalizes curiosity—agents are driven to interact with states that challenge their own models, thereby self-supervising their curriculum and emergence of complex behavior (Haber et al., 2018).

2. Action-Conditional and Bidirectional Representation Learning

Recent advances in self-predictive RL focus on learning latent representations that serve as compact predictors of future internal states.

• The BYOL (“bootstrap-your-own-latent”) approach and its action-conditional variants (BYOL-AC) cast self-predictive learning as minimizing internal prediction errors across time and actions:

$$\mathrm{BYOL{-}AC}(\Phi, \{P_a\}) = \mathbb{E}_{x,a,y}\bigl[ \| P_a^T \Phi^T x - \mathop{\mathrm{sg}}(\Phi^T y) \|^2 \bigr]$$

• Action-conditional objectives enable richer representations by differentiating dynamics induced by individual actions, rather than blending them through policy-averaged transitions. The learned representations are characterized by trace equations that, under simplifying assumptions, correspond to the top eigenvectors of mean squared per-action dynamics matrices.
• Bidirectional self-predictive learning (forward and backward sequence prediction) is shown to perform spectral decomposition and, in a tabular setting, recovers both left and right singular vectors of the transition matrix; this dual signal is useful for richer representation capture and value function estimation (Tang et al., 2022, Khetarpal et al., 4 Jun 2024).
• Empirical results highlight that action conditioning leads to superior RL performance versus policy-conditioned baselines across benchmark domains.

3. Self-Adaptive Goal Mechanisms and Transferability

Self-predictive agents are further augmented by adaptive goal mechanisms, allowing on-the-fly adjustment of priorities and strategies.

• The “Direct Future Prediction” (DFP) framework leverages predictive models that focus only on key measurements (e.g., ammunition, health, enemy kills). A small evolving Goal-ANN uses current measurements to dynamically output goal vectors, which selectively weight the importance of each predicted outcome.
• This architecture enables rapid adaptation and transfer: an agent trained on one goal-set can, via the Goal-ANN, be repurposed for distinct environments (e.g., switching from aggressive to defensive behavior as resources or threats vary) without retraining the core predictive model (Ellefsen et al., 2019).
• Evaluations show that evolved strategies outperform both static and hand-coded adaptive policies, with robust statistical evidence supporting the superiority of adaptive self-predictive goals.

4. Self-Predictive Models for Explanation and Semantic Understanding

Agents leveraging self-predictive models can produce human-interpretable explanations based on expectations of the future and develop decomposable semantics.

• Embedded Self-Prediction (ESP) architectures use generalized value functions (GVFs) to represent action-values as aggregated, discounted predictions over human-provided semantic features (e.g., safety, fuel consumption, task damage).
• Action preferences are explained through contrastive analysis of predicted future properties, concretely quantified via integrated gradients and minimal sufficient explanations (MSX). This yields concise, sound reasons for why one action is chosen over another in terms of future outcomes (Lin et al., 2020).
• In world-model-based agents trained with auxiliary predictive losses, emergent internal representations encode factual, compositional, and relational knowledge about the environment (e.g., object identity, spatial relationships). Post hoc question-answering probes (applied without backpropagation) can decode these representations, uncovering the semantic structure accumulated during predictive learning (Das et al., 2020).

5. Synchronization and Self-Triggering in Multi-Agent Systems

Distributed self-predictive mechanisms extend to multi-agent systems through synchronization parameters and self-triggered control strategies.

• Self-triggered Distributed Model Predictive Control (DMPC) allows agents to determine autonomously when to update their control inputs by ensuring a Lyapunov candidate function decreases sufficiently over predictive steps. Recursive feasibility and input-to-state stability are guaranteed via terminal constraints and disturbance bounding.
• Coordination emerges via the synchronizing of one-dimensional parameter sequences exchanged among agents. The agents’ OCP includes cost terms penalizing divergence among these parameters; convergence is achieved even with asynchronous updates and without sharing full state vectors.
• Simulation results demonstrate reduced communication load, robust convergence, and effective formation tracking using only local predictions and synchronization (Chen et al., 17 May 2024).

6. Symbolic Self-Prediction and Autonomous Self-Evolving Agents

Symbolic learning frameworks enable self-predictive agents to autonomously optimize themselves in natural language and tool-centric environments.

• “Agent symbolic learning” reconceptualizes language agents as symbolic networks with prompts, tool choices, and pipeline structures as learnable “weights.” Forward passes record trajectories, and language loss is evaluated via LLMs and prompt-based scoring.
• Gradient-like updates (“language gradients”) are then performed using reflective analysis, guiding modifications to prompts and tools via symbolic optimizers.
• Proof-of-concept experiments show that these agents can improve performance on both classic benchmarks (e.g., HotPotQA, MATH) and emergent tasks (software synthesis, creative writing) after deployment, indicating the feasibility of self-evolving, continuously self-predictive agents (Zhou et al., 26 Jun 2024).

7. Implications and Future Directions

The self-predictive agent paradigm provides foundational mechanisms for advanced autonomous intelligence across domains.

• Intrinsic motivational schemes drive self-supervised exploration, enabling agents to generate diverse, informative training data and foster the emergence of complex, transferable behaviors.
• Action-conditional and bidirectional prediction objectives supply theoretically justified frameworks for constructing robust world-models essential for planning, value estimation, and generalization.
• Adaptive goal selection and synchronization control facilitate rapid transfer and coordinated behavior in continually evolving, multi-agent environments.
• Symbolic frameworks and LLM-based agentic pipelines can autonomously extend their prediction and reasoning capabilities, highlighting pathways toward agents whose self-predictive faculties encompass both numeric and symbolic representations.
• Empirical evidence from a variety of RL and agentic benchmarks consistently demonstrates that self-predictive mechanisms improve data efficiency, explainability, transfer, and robustness relative to non-predictive baselines.

This multifaceted landscape situates self-predictive agents as a central topic in autonomous agent research, weaving together mathematical modeling, intrinsic motivation, adaptive control, interpretability, and self-evolution to support flexible, general, and safe artificial intelligence systems.