Open-Ended Learning Frameworks in AI

Updated 1 July 2025

Open-ended learning frameworks enable AI agents to autonomously discover and acquire skills and knowledge indefinitely in dynamic environments without predefined tasks.
They leverage intrinsic motivations such as competence progress and novelty, often employing hierarchical architectures for autonomous exploration and task selection.
These frameworks are applied in robotics and reinforcement learning to achieve lifelong skill acquisition and handle complex, interdependent tasks more effectively.

Open-ended learning frameworks encompass a class of machine learning and robotic control architectures designed to enable agents to autonomously acquire a diverse repertoire of skills and knowledge, without prior specification of all possible tasks or goals. These frameworks typically emphasize intrinsic motivations, continual and autonomous exploration, adaptive curriculum generation, and mechanisms for handling interdependent or evolving task structures. The following sections elucidate the main concepts, methodologies, and research findings associated with open-ended learning frameworks, as developed and evaluated in the literature.

1. Key Principles and Definitions

At its core, open-ended learning is defined by an agent’s ability to perpetually encounter and learn new tasks or categories in an environment where the entirety of possible goals, object classes, or challenges is not known a priori. Unlike traditional supervised or reinforcement learning, open-ended learning removes the assumption of a fixed problem set, instead supporting incremental, interactive, and lifelong skill acquisition (1907.10932, 1912.09539). This involves:

Autonomous goal discovery and generation ("self-generated goals" or autotelic behavior).
Adaptation to dynamically changing environments and task dependencies.
Continual refinement of knowledge, with retention of previously acquired competencies (addressing catastrophic forgetting).
Task-agnostic exploration driven primarily by intrinsic motivation signals, such as novelty, curiosity, or competence progress (1905.02690).

Distinct from continual or lifelong learning, truly open-ended learning frameworks require unbounded ongoing novelty—an agent’s developmental trajectory is not a predefined curriculum, but emerges from interaction and intrinsic signals (2311.00344).

2. Intrinsic Motivation and Task Selection

A central mechanism in open-ended learning is the deployment of intrinsic motivations (IMs) to direct exploration and skill acquisition in the absence of dense external rewards. Common intrinsic motivations include:

Competence Improvement: Agents measure their progress toward goals (competence $C_g$ ), prioritizing practice where learning speed (improvement $\delta C$ ) is greatest. This induces an adaptive, self-organized curriculum that guides attention toward new or challenging tasks (1905.02690).
Novelty and Curiosity: Learning is biased toward states, objects, or tasks that are novel relative to the agent’s experience, promoting the expansion of its behavioral repertoire (1912.09539).
Strategic intrinsic motivation: Some frameworks employ mechanisms that alternate between discovery-driven and mastery-driven phases, balancing exploration for new goals and exploitation for improving known skills (2506.18454).

Formally, if $C_g(t)$ denotes competence for goal $g$ at time $t$ , intrinsic reward is defined as $\delta C_g = C_g(t + h) - C_g(t)$ , with $h$ a time horizon. Task selection may then be framed as: $\Pi^* = \underset{\Pi}{\text{argmax}} \, \delta C(\Pi_t)$ with $\Pi_t$ the policy or goal at time $t$ . In more advanced variants, the process can be extended to state-dependent and temporally sequenced tasks using Markov Decision Processes (MDPs) (1905.02690).

3. Hierarchical and Decision-theoretic Architectures

Open-ended learning agents often rely on hierarchical architectures that separate high-level goal or task selection from low-level skill learning (1912.09539, 2506.18454). Typical components include:

Goal Selector (GS): Responsible for choosing which task or goal to focus on, based on IM signals. It is modeled as:
- A multi-armed bandit (no context),
- A contextual bandit (selected according to current environmental state),
- Or a full MDP, particularly when there are interdependencies between tasks or goals.
Low-level Experts: Each expert learns a policy for a specific goal/task, often using actor-critic or similar RL algorithms.
Meta-policy modules: For handling tasks with dependencies, a sub-goal selector may sequence goals, employing Q-learning to propagate value through the dependency graph (2506.18454).
Memory and knowledge management subsystems: These enable the incremental addition of object categories, skills, or grasp configurations, and handle the archiving or forgetting of outdated information (1912.09539).

A schematic illustration of such an architecture is:

+----------------------+
|   Goal Selector (GS) |-----> [selects goal/task]
|  [IM, bandit/MDP]    |
+----------------------+
            |
            v
+-----------------------------+
| Low-level Experts           |
| [per-goal neural networks]  |
+-----------------------------+

4. Dealing with Interdependent and Evolving Task Structures

A key challenge in real-world open-ended learning is handling task interdependencies, where learning some tasks is contingent on mastering others (e.g., complex object manipulations requiring basic grasping first) (1905.02690, 2506.18454). Effective frameworks treat task selection as an MDP:

State encodes which tasks/goals have been achieved (and possibly the environment context).
Actions: Select next goal/policy to train or execute.
Rewards: Based on competence progress $\delta C$ , and critically, propagated through dependencies by Q-learning.

This approach ensures that agents not only learn which individual goals to pursue, but discover optimal sequences for cumulative skill acquisition. Experiments confirm that such MDP-based selection enables agents to solve deeply interdependent tasks significantly faster and more reliably than bandit-based or context-free selection (1905.02690).

When the environment or goal structures change (non-stationarity), open-ended frameworks employ adaptive mechanisms such as dynamic bandit indices, competence-based modulation, and hierarchical arbitration for rapidly redirecting learning priorities (2506.18454).

5. Application Domains and Empirical Validation

Open-ended learning frameworks have been applied in:

Robotics: Incremental object perception and grasp affordance learning, where object and action categories are not fixed but are discovered over time via experience and human interaction (1907.10932, 1912.09539).
Manipulation and Skill Chaining: Humanoid robots autonomously learn curricula of reaching and activation tasks with dependencies (1905.02690).
Interactive, human-in-the-loop scenarios: Architectures enable robots to learn new concepts or affordances from kinesthetic and verbal teaching, and to correct errors based on external feedback (1912.09539).
Service and assistive robotics, generalization to lifelong learning settings: These frameworks are designed for scalability across domains, supporting real-time adaptation, autonomous category discovery, and cumulative skill buildup.

Experiments confirm that hierarchical and intrinsically motivated architectures achieve superior efficiency, breadth of skill acquisition, and robustness to non-stationary contexts compared to random exploration or non-hierarchical baselines (1905.02690, 2506.18454). Evaluation metrics include the number of learned categories, learning speed (iterations per category), overall accuracy (GCA, APA), and success in handling interdependent scenarios.

6. Limitations and Directions for Further Research

Challenges identified in the current generation of open-ended learning frameworks include:

Scalability: Efficiently handling large numbers of tasks/goals, especially with complex, hierarchical dependencies and in real-world environments subject to perceptual noise (1905.02690).
Hierarchical Discovery: Developing systems that can autonomously uncover and exploit multi-level task decompositions, not only "flat" dependencies.
Generalization to Continuous and Unstructured Domains: Extending from discrete and well-partitioned goal spaces to continuous, parameterized, or semantically rich task sets.
Integration of Additional Motivational Signals: Exploring how other forms of intrinsic motivation (e.g., surprise, uncertainty, epistemic value) can augment competence-based progress.
Memory/Resource Constraints: As open-ended learning proceeds, agents must manage memory (e.g., instance bases, skill repertoires) to ensure scalability and prevent unbounded growth (1912.09539).
Alignment with User Purposes: Ensuring that autonomously acquired skills remain relevant and safe for deployment in human-centered environments, possibly via purpose/desire-goal alignment architectures (2403.02514).

Advances in interactive protocols, continual online evaluation, and human-in-the-loop design are expected to further ground and expand the practical impact of open-ended learning frameworks.

7. Contributions to the Field

Open-ended learning frameworks have established new benchmarks for autonomy, adaptability, and scalability in artificial agents, particularly in robotics and multi-task reinforcement learning. Their central innovations include the use of learning progress as an intrinsic reward, the explicit handling of interdependent tasks as MDPs with reward propagation, hierarchical decision architectures for cumulative skill chaining, and integration with interactive, real-world learning protocols. These developments have produced practical systems capable of robust category and skills acquisition in dynamic environments, provided measurable improvements over hand-crafted curricula or simple exploration, and opened new avenues for lifelong, developmental, and self-directed AI research.