Learning to Control Without Labels: A New Approach to Reinforcement Learning

Author: Denis Avetisyan

Researchers have developed a novel method for training agents using both labeled actions and unlabeled trajectories, significantly reducing the need for costly and time-consuming data annotation.

A latent action representation enables the combination of extensive action-free data with limited action-conditioned data, facilitating offline reinforcement learning paradigms such as C-LAPc-lap and offering a pathway to leverage both data sources effectively-a technique grounded in the principle that a comprehensive dataset, even with varying levels of conditioning, yields a more robust and provable learning outcome.

Latent Action World Models effectively combine action-labeled and action-free data to improve offline reinforcement learning performance and data efficiency.

While reinforcement learning often demands extensive labeled action data, humans readily learn from both direct interaction and passive observation. This limitation motivates the work presented in ‘Latent Action World Models for Control with Unlabeled Trajectories’, which introduces a novel approach to world modeling capable of leveraging both action-conditioned and action-free data via a shared latent action representation. By aligning observed control signals with actions inferred from unlabeled trajectories, this method enables training a single dynamics model with significantly reduced reliance on labeled samples. Could this bridging of offline reinforcement learning and action-free training unlock more efficient and robust control policies in complex environments?

The Imperative of Sample Efficiency in Reinforcement Learning

Conventional Deep Reinforcement Learning algorithms typically demand a substantial volume of environmental interaction to develop robust and effective policies. This presents a significant impediment to practical deployment, as acquiring this level of active data can be prohibitively expensive, time-consuming, and occasionally even dangerous-consider scenarios like robotics, autonomous driving, or healthcare. The core issue lies in the sequential nature of reinforcement learning; each action taken influences subsequent states, requiring numerous trials and errors to map optimal behaviors. Consequently, the need for extensive interaction fundamentally limits the applicability of Deep RL to environments where such data collection is feasible and safe, motivating research into more sample-efficient learning paradigms.

The conventional approach to training agents using deep reinforcement learning often demands an immense number of interactions with the environment, posing significant challenges for practical deployment. This reliance on active data collection isn’t merely a matter of computational resources; it represents a substantial logistical and financial burden, especially when dealing with systems that require physical operation. Consider scenarios involving robotics, autonomous driving, or even complex simulations – each trial and error can be costly in terms of time, energy, and potential damage. Moreover, some environments are inherently dangerous or inaccessible, making extensive active exploration simply impractical. The need to minimize this active data requirement is therefore paramount, driving research into methods that can learn effectively from limited experience or, crucially, from data collected without direct agent intervention.

The increasing prevalence of passively collected data, often termed Action-Free Data, offers a compelling alternative to the traditional, interaction-dependent paradigm of Deep Reinforcement Learning. Unlike actively gathered data generated through exploration, this information arises from observation without requiring an agent to take actions-think of video recordings of human behavior or logs of system operations. However, effectively utilizing this resource necessitates innovative methodologies, as standard reinforcement learning algorithms rely heavily on reward signals obtained from direct action feedback. Researchers are now exploring techniques like behavioral cloning, inverse reinforcement learning, and various forms of self-supervised learning to distill knowledge from these observation-only datasets, effectively ‘pre-training’ agents or learning robust representations that can then be fine-tuned with limited active interaction. This shift promises to dramatically improve sample efficiency, enabling the application of reinforcement learning to domains where active data collection is expensive, risky, or simply infeasible.

Return distributions across three locomotion tasks reveal that training with the plan2explore dataset consistently outperforms datasets based on expert or replay buffers.

Offline Reinforcement Learning: Addressing Distribution Shift and Extrapolation Error

Offline Reinforcement Learning (RL) addresses the sample inefficiency common in traditional RL by enabling agents to learn policies from pre-collected, static datasets. However, this approach introduces significant challenges stemming from distribution shift and extrapolation error. Distribution shift occurs because the data used for training may not accurately reflect the states the agent encounters when deployed under the learned policy. Extrapolation error arises when the agent is required to predict outcomes for state-action pairs outside the support of the training data; since the agent hasn’t observed these scenarios, predictions are inherently unreliable. Both issues can lead to drastically overestimated rewards and unstable learning, necessitating specialized algorithms designed to mitigate these effects and improve generalization performance.

Latent Action Representation (LAR) addresses limitations of directly learning policies from raw action spaces by mapping high-dimensional, discrete, or continuous actions into a lower-dimensional latent space. This is achieved through techniques like variational autoencoders (VAEs) or similar dimensionality reduction methods, effectively compressing the action space while preserving essential information for policy learning. The resulting latent space allows the agent to generalize more effectively, as similar actions are clustered together, and the policy learns to operate on this compressed representation rather than directly predicting raw actions. This compression facilitates extrapolation to unseen states and actions, mitigating the distribution shift common in offline reinforcement learning, and enabling more robust policy optimization even with limited data coverage in the original action space. The learned latent space is typically continuous, allowing for smoother exploration and improved policy gradients.

Latent action representations are critical for offline reinforcement learning because they facilitate generalization beyond the confines of the static dataset. Traditional policy learning methods struggle with extrapolation error – the inability to accurately predict outcomes for actions not well-represented in the training data. By learning a compressed, lower-dimensional representation of actions, the agent can infer plausible outcomes for novel action combinations. This is achieved by mapping high-dimensional action spaces into a latent space where distances correlate with behavioral similarity; thus, unseen actions close to observed actions in this latent space are more likely to yield predictable, and therefore safe, results. This improved generalization capability allows the agent to effectively learn policies from offline data without suffering from the severe performance degradation that often occurs when extrapolating from limited experience.

World Models: Predictive Dynamics for Enhanced Learning Efficiency

The World Model paradigm addresses limitations in reinforcement learning by enabling agents to learn an internal representation of the environment’s dynamics. This learned model functions as a predictive tool, allowing the agent to forecast the outcomes of its actions without direct environmental interaction. Instead of relying solely on trial-and-error within the actual environment, the agent can simulate potential scenarios within its internal model, effectively planning and evaluating different courses of action. This simulation capability significantly enhances sample efficiency, as the agent can accumulate experience and refine its policies through simulated interactions, reducing the need for costly and time-consuming real-world experimentation. The accuracy of this predictive model is paramount to the effectiveness of the paradigm.

Generative models form the core of world model architectures by learning the underlying probability distribution of an environment’s state transitions. These models take the agent’s current internal state – representing its beliefs about the world – as input and produce predictions of future observations and potential actions. The models are trained on sequences of state, action, and reward data, effectively learning to generate plausible continuations of experienced trajectories. This generative process allows the agent to synthesize data, creating a simulated experience independent of real-world interaction, and is typically implemented using deep neural networks capable of representing complex, high-dimensional distributions. The output of these models is not a single prediction, but rather a probability distribution over possible outcomes, enabling the agent to assess risk and uncertainty in its predictions.

Variational Inference (VI) is a crucial technique for approximating the complex posterior probability distributions encountered in world models, as exact calculation is often intractable. VI operates by defining a simpler, tractable distribution – the variational distribution – and minimizing the Kullback-Leibler (KL) Divergence, $D_{KL}(q(z|x) || p(z|x))$, between this approximation and the true posterior $p(z|x)$, where $z$ represents the latent state and $x$ the observed data. This minimization process effectively finds the closest approximation within a defined family of distributions, enabling efficient learning and inference despite the intractability of the true posterior. KL Divergence, measured in bits or nats, quantifies the information lost when $q$ is used instead of $p$, serving as a primary metric for evaluating the quality of the variational approximation and guiding the learning process.

The capacity to predict action consequences enables agents to perform model-based planning and optimization entirely within a simulated environment, circumventing the need for real-world interaction. This is achieved by utilizing the learned world model to forecast future states resulting from various action sequences. The agent can then evaluate these predicted trajectories based on a defined reward function, selecting the action sequence that maximizes cumulative reward. This process, often implemented through algorithms like Monte Carlo Tree Search, allows for efficient exploration of potential behaviors and identification of optimal policies without incurring the costs or risks associated with physical experimentation. Consequently, learning can proceed more rapidly and safely, particularly in environments where real-world interactions are expensive, dangerous, or time-consuming.

Inferring Action from Observation: The Power of Inverse Dynamics

The Inverse Dynamics Model (IDM) functions as a critical component in inferential systems by establishing a probabilistic link between observed states and the actions that likely generated those states. Given an observed transition from one state, $s_t$, to another, $s_{t+1}$, the IDM estimates a probability distribution over the possible actions that could have caused this transition. This is achieved through a learned mapping, typically a neural network, trained on data consisting of state transitions and corresponding actions. The output of the IDM is not a single predicted action, but rather a probability distribution, reflecting the inherent ambiguity in inferring actions from observations alone. This inferred action distribution is then used to refine the agent’s understanding of the environment and improve decision-making capabilities.

The Inverse Dynamics Model (IDM) enhances the World Model by providing predictions of the actions that transition the agent between observed states. This augmentation moves beyond simply predicting the next state given the current state and action – the conventional forward model – to instead infer the action that most likely caused the observed state transition. By incorporating inferred actions into the World Model, the agent develops a more comprehensive understanding of the environment’s dynamics, effectively learning a bidirectional representation of state transitions. This allows for improved planning, counterfactual reasoning, and more robust prediction capabilities, as the model accounts for both the effects of actions and the actions that likely resulted in specific observations.

The utility of the Inverse Dynamics Model (IDM) is significantly enhanced when processing Action-Free Data, which consists of observational sequences lacking corresponding action signals. In these scenarios, the IDM functions to infer the actions that most plausibly generated the observed state transitions. This capability circumvents the requirement for direct action feedback during training, allowing the agent to establish correlations between states and potential actions solely through observation. Consequently, the agent can build a predictive model of environmental dynamics and improve its World Model even in the absence of explicit action labels, effectively leveraging passive data for learning and planning.

Demonstrating Progress: Validation with the DeepMind Control Suite

Recent advancements in reinforcement learning are powerfully demonstrated by algorithms like Dreamer, which have achieved state-of-the-art results on the challenging DeepMind Control Suite. This suite, a collection of continuous control tasks, rigorously tests an agent’s ability to master complex motor skills, such as manipulating articulated figures or navigating dynamic environments. Dreamer’s success isn’t merely theoretical; it consistently outperforms previous approaches across a range of these tasks, exhibiting robust locomotion and skillful manipulation. This performance validates the underlying principles of Offline Reinforcement Learning and World Models, suggesting a viable path toward creating agents capable of learning effectively from limited data and generalizing to new, unseen scenarios. The ability to achieve such high levels of control highlights the potential for these techniques to drive innovation in robotics, automation, and other fields requiring intelligent, adaptive behavior.

The DeepMind Control Suite serves as a crucial cornerstone in the advancement of reinforcement learning by offering a rigorously defined and standardized set of environments. This benchmark suite allows researchers to move beyond subjective evaluations and facilitates objective comparisons of diverse algorithms, fostering rapid progress in the field. By providing consistent challenges – ranging from manipulating articulated figures to navigating complex terrains – the Control Suite isolates algorithm performance, enabling precise measurement of strengths and weaknesses. The suite’s design emphasizes both ease of use and scalability, promoting reproducibility and encouraging the development of more robust and efficient agents capable of tackling increasingly complex tasks. This standardized approach not only accelerates research but also builds confidence in the generalizability of new methodologies, paving the way for real-world applications of reinforcement learning.

The recent advancements embodied by algorithms like Dreamer signal a paradigm shift in reinforcement learning, moving beyond the constraints of traditional methods. These successes highlight the power of Offline RL and World Models to construct capable agents even when real-time interaction with an environment is limited or impractical. By learning from pre-collected datasets – rather than requiring constant trial-and-error – these techniques dramatically improve sample efficiency and facilitate learning in complex scenarios. The ability to build robust agents from static data opens doors to applications where data collection is expensive, dangerous, or time-consuming, promising a future where intelligent systems can be deployed more rapidly and effectively across a wider range of real-world problems. This approach not only enhances efficiency but also fosters the development of agents capable of generalizing to unseen situations, ultimately paving the way for more adaptable and resilient artificial intelligence.

This research introduces Latent Action World Models (LAWM), a novel approach to reinforcement learning that demonstrably surpasses existing state-of-the-art algorithms. Rigorous testing across the DeepMind Control Suite – specifically the challenging cheetah-run, walker-walk, and hopper-stand environments – reveals a significant and consistent performance improvement. LAWM achieves this by learning a compressed, internal representation of the environment, allowing for more efficient planning and decision-making. The model’s superior results highlight the potential of learning effective policies directly from environmental observations, offering a promising direction for developing more capable and adaptable agents in complex simulated and real-world scenarios.

Latent Action World Models (LAWM) exhibit a remarkable capacity for efficient learning, successfully extracting robust policies from datasets where action-conditioned data comprises a mere 5% of the total information. This proficiency addresses a critical challenge in reinforcement learning – the scarcity of labeled action data – by prioritizing learning from predominantly unlabelled observations. The model effectively constructs a predictive world model using this largely action-free data, and subsequently learns optimal behaviors within this simulated environment. This demonstrates a significant step towards more practical and scalable reinforcement learning systems, as it reduces the reliance on expensive and time-consuming data collection requiring explicit action labels, opening avenues for learning from readily available observational data streams.

The pursuit of robust control strategies, as demonstrated in this work on Latent Action World Models, echoes a fundamental tenet of mathematical rigor. The ability to effectively leverage both labeled and unlabeled data-to build a predictive model from incomplete information-speaks to the elegance of a well-defined system. As Paul Erdős once stated, “A mathematician knows a lot of things, but a physicist knows a few.” This sentiment, while playful, highlights the need for provable foundations; LAWM’s approach offers precisely that-a system built not on empirical success alone, but on a consistent framework for understanding and predicting dynamic systems, even with limited action information. The core idea of combining data sources, therefore, isn’t merely a practical improvement, but a step towards mathematical purity in the realm of reinforcement learning.

Beyond Simulation: Charting the Future of World Models

The presented work, while a demonstrable improvement in leveraging unlabeled data, merely scratches the surface of a fundamental challenge. The efficacy of Latent Action World Models, like all model-based reinforcement learning, remains tethered to the fidelity of the learned dynamics. A perfectly accurate model, of course, is an asymptotic ideal, and the inevitable divergence between simulation and reality necessitates continued investigation into robust control strategies that gracefully handle model error. The true measure of progress will not be the volume of unlabeled data consumed, but the demonstrable reduction in sample complexity achieved in genuinely novel environments.

A critical, often overlooked, limitation lies in the implicit assumption of stationarity. Real-world systems are rarely static; they evolve, adapt, and exhibit unforeseen behaviors. Future research must address the problem of continual learning within world models – the ability to seamlessly incorporate new experiences without catastrophic forgetting or requiring complete retraining. This demands a shift in focus from simply predicting the next state, to actively quantifying and managing model uncertainty.

Ultimately, the pursuit of increasingly complex world models risks falling into a trap of diminishing returns. A more elegant solution may lie not in simulating the world with ever-greater fidelity, but in developing algorithms that are intrinsically robust to imperfect knowledge. The holy grail remains: a control policy provably optimal, or at least bounded in performance, even when operating under the shadow of incomplete or inaccurate information.

Original article: https://arxiv.org/pdf/2512.10016.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/