Task-Agnostic Exploration in RL

• The paper introduces a novel algorithm that collects diverse, informative trajectories for world model learning in RL, decoupling data collection from external rewards.
• It leverages Model-Based Active Exploration (MAX) with an ensemble of forward dynamics models to prioritize novel transitions using measures like information gain and ensemble uncertainty.
• Empirical results demonstrate significant improvements in sample efficiency and data reusability, supporting flexible adaptation to multiple downstream tasks.

A task-agnostic exploration-based data collection algorithm is a class of methods in reinforcement learning (RL) and control that actively seeks to collect diverse, informative trajectories from an environment without reference to any specific, extrinsic reward function or downstream task objective. The principal goal is to gather data that supports the efficient learning of world models, policies, or outcome repertoires capable of being leveraged for multiple, arbitrary future tasks. Such algorithms are foundational for sample-efficient, flexible RL and increasingly underpin research in model learning, robotic generalization, offline RL, and curriculum generation.

1. Principles and Motivation

Task-agnostic exploration is motivated by the need to decouple data collection from task-specific optimization. In many scientific and practical settings—including robotics, lifelong learning, and offline RL—the future tasks of interest are not known at exploration time, or reward signals are sparse, delayed, or imprecisely specified. Task-agnostic methods emphasize the accumulation of knowledge about the environment's dynamics or state/control space, endowing agents with a rich, reusable experience base that can support adaptation to multiple or unforeseen objectives.

Typical principles include:

• Planning or acting with respect to intrinsic, task-independent objectives (novelty, uncertainty, surprise, state entropy)
• Covering the state-action space as broadly and efficiently as possible
• Collecting outcome-diverse data rather than optimizing towards one reward or skill
• Ensuring the collected dataset enables learning high-quality world models or robust policies for downstream adaptation

2. Algorithmic Mechanics: Model-Based Active Exploration (MAX)

A canonical example is Model-Based Active eXploration (MAX), a model-based RL approach in which an ensemble of forward dynamics models is trained on accumulated experience. Instead of using a standard reward, MAX replaces the exploration objective with a measure of novelty estimated via model disagreement or information gain.

At each decision point, MAX constructs an internal exploration Markov Decision Process (MDP), retaining the external state and action spaces but assigning reward as a function of novelty. This reward is typically computed as:

• Information gain for a transition $(s, a, s')$: $IG(s, a, s') = D_{KL}(P(T|s, a, s') || P(T))$, where $P(T)$ is the prior distribution over transition functions
• Ensemble-based uncertainty: The utility of a state-action pair is $u(s, a) = JSD\{P(S | s, a, t)\ |\ t \sim P(T)\}$ for ensembles in discrete environments, or estimated using Jensen–Rényi divergence in continuous spaces

MAX then uses planning methods—such as Monte Carlo Tree Search or sampling-based policy optimization—to find action sequences that maximize expected cumulative novelty. Only the most informative or uncertain transitions, with respect to the current model beliefs, are prioritized for execution.

3. Novelty and Intrinsic Motivation Metrics

Measuring the novelty or informativeness of candidate transitions is central to all task-agnostic exploration algorithms. Common approaches include:

• Jensen–Shannon Divergence (JSD) between ensemble model predictions in discrete state spaces:

$$u(s, a) \approx H\left(\frac{1}{N}\sum_{i=1}^N P(S|s, a, t_i)\right) - \frac{1}{N}\sum_{i=1}^N H(P(S|s, a, t_i))$$

where $H(\cdot)$ is the entropy function and $\{t_i\}$ are ensemble models.

• Jensen–Rényi Divergence via Rényi entropy in high-dimensional continuous environments, as Shannon entropy becomes computationally intractable.
• Expected information gain, capturing how much an agent would learn (in the Bayesian sense) by executing an action and observing its outcome.

The use of model uncertainty as an intrinsic reward systematically favors transitions that reduce epistemic uncertainty in the world model as opposed to those with high aleatoric noise. This discriminative capacity is a crucial advance over purely reactive exploration methods, which may waste effort on unpredictably stochastic but ultimately unlearnable transitions.

4. Empirical Data Efficiency and Performance

Task-agnostic exploration, and in particular MAX, demonstrates substantial improvements in exploration efficiency over reactive, bonus-based approaches such as Exploration Bonus Deep Q-Network (EB-DQN) or Bootstrapped DQN. In empirical settings:

• In semi-random discrete environments (e.g., chain tasks with stochastic transitions), MAX explores close to 100% of transitions in an order of magnitude fewer episodes than baselines.
• In high-dimensional continuous settings (e.g., Ant Maze, Half Cheetah), MAX-driven data collection yields more rapid coverage of remote or hard-to-discover regions in the environment.
• Downstream task performance benefits directly from the improved quality and diversity of data. MAX-collected datasets enable accurate, task-agnostic models; when subsequently trained on a specific task, agents achieve higher final performance than when trained with data from reactive strategies.

These outcomes establish that principled, planned exploration using model-based novelty yields not just broader coverage but more useful, reusable data for a variety of tasks.

5. Construction and Use of Task-Agnostic Models

An essential property of task-agnostic exploration algorithms is their ability to produce predictive world models or behavioral repertoires that can be leveraged for any future task. Because task-agnostic data collection disregards external rewards, the agent’s experiences span the full range of controllable dynamics, not just the subset favored by a single task. This approach is particularly powerful in transfer learning settings, multitask RL, or for model-based planners that need accurate rollouts in regions that would otherwise be neglected under narrow task-driven supervision.

Once collected, these models or data can be:

• Used directly for policy optimization towards any newly specified reward function or external task
• Composed to form skill repertoires or for meta-learning over tasks
• Evaluated for generalization capacity and used as building blocks for hierarchical policies

6. Comparative Advantages and Limitations

In comparison to reactive, task-driven, or curiosity-bonus-based data collection, task-agnostic exploration-based algorithms such as MAX offer several advantages:

• Proactive exploration: By simulating model rollouts and planning where to explore, agents avoid over-sampling well-known regions and balance coverage of accessible but under-explored states.
• Data reuse: Once acquired, task-agnostic data can support any number of downstream tasks without needing further interaction. This reduces the sample complexity for new objectives.
• Discrimination between epistemic and aleatoric uncertainty: Model-based methods distinguish between uncertainty arising from lack of knowledge and randomness inherent to the environment.

However, limitations may include:

• Additional computational overhead for training and maintaining model ensembles, especially in high-dimensional settings.
• Potential misspecification of the novelty metric, which may underemphasize rarely encountered but task-critical transitions unless the model ensemble is robust.
• Infeasibility in highly stochastic environments where epistemic and aleatoric uncertainty compete.

7. Mathematical Summary Table

Measure / Update	Discrete / Continuous	Formula / Description
Information Gain	Both	$IG(s,a,s') = D_{KL}(P(T|s,a,s') \| P(T))$
Ensemble Uncertainty (JSD)	Discrete	$u(s,a) = JSD\{P(S|s,a,t)\}$
Ensemble Uncertainty (Rényi)	Continuous	$u(s,a)$ via Jensen–Rényi Divergence
Ensemble Utility Estimate	Both	$$u(s,a) \approx H\left(\frac{1}{N}\sum P_i\right) - \frac{1}{N} \sum H(P_i)$$
Planning Objective	Both	Plan using internal MDP with reward = novelty/uncertainty

These mathematical expressions capture the core computation underlying value of information-driven exploration in model-based task-agnostic algorithms.

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Task-agnostic exploration-based data collection algorithms, exemplified by MAX, reframe the RL data gathering process from reactive response to active, theoretically grounded planning for novelty and uncertainty reduction. This framework enables sample-efficient, transferable, and robust model learning for arbitrary future downstream tasks, marking a key development in reinforcement learning methodology (Shyam et al., 2018).