Planning for the Future: A New Approach to Offline Reinforcement Learning

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Author: Denis Avetisyan

Researchers have developed a novel algorithm that tackles long-term decision-making in offline reinforcement learning, improving performance on challenging datasets.

The algorithm demonstrates robust performance on a D4RL dataset by modulating a rollout truncation threshold-defined by the uncertainty quantile $\zeta$ ranging from 0.9 to 1.0-and achieving normalized scores in real environments without relying on conservative methods, as evidenced by the estimated Q-values and the median with interquartile range observed across 100 training rollouts.
The algorithm demonstrates robust performance on a D4RL dataset by modulating a rollout truncation threshold-defined by the uncertainty quantile $\zeta$ ranging from 0.9 to 1.0-and achieving normalized scores in real environments without relying on conservative methods, as evidenced by the estimated Q-values and the median with interquartile range observed across 100 training rollouts.

Neubay, a Bayesian model-based RL method utilizing recurrent neural networks, overcomes compounding errors and value overestimation for robust long-horizon planning.

Popular offline reinforcement learning methods often rely on penalizing actions outside the training data or limiting planning horizons, yet this conservatism isn’t universally optimal. This work, ‘Long-Horizon Model-Based Offline Reinforcement Learning Without Conservatism’, revisits a Bayesian approach to tackle uncertainty in offline data by learning a distribution over world models and training agents to maximize expected rewards-enabling robust generalization. The resulting algorithm, Neubay, achieves state-of-the-art performance on benchmark tasks, notably succeeding with planning horizons previously considered impractical, and demonstrates superior results on low-quality datasets. Does this neutral Bayesian principle represent a fundamental shift in how we approach offline and model-based reinforcement learning?

The Challenge of Static Data in Reinforcement Learning

Conventional reinforcement learning algorithms typically demand a substantial number of interactions with an environment to effectively learn an optimal policy. This reliance on active data collection presents significant hurdles in numerous real-world scenarios. Consider applications like robotics, healthcare, or financial trading – each trial can be time-consuming, expensive, or even dangerous. For example, training a robot to perform a complex manipulation task requires countless physical attempts, potentially leading to wear and tear or safety concerns. Similarly, in medical treatment planning, direct experimentation on patients is ethically and practically impossible. The sheer cost of acquiring data through real-time interaction often renders traditional reinforcement learning infeasible, creating a pressing need for methods that can learn from pre-collected, static datasets.

The promise of offline reinforcement learning lies in its ability to bypass the need for continuous interaction with an environment, instead deriving knowledge from pre-collected, static datasets. However, this approach isn’t without considerable hurdles. Unlike traditional RL where an agent can actively explore and correct mistakes, offline RL struggles with generalization – effectively applying learned strategies to states not well-represented in the dataset. This is compounded by issues of stability; small changes in the data or algorithm can lead to drastically different, and potentially unreliable, policies. Consequently, algorithms must contend with distribution shift – the discrepancy between the data used for learning and the states encountered during deployment – and avoid extrapolating beyond the bounds of observed experience, demanding innovative techniques to ensure robust and dependable performance.

A persistent issue in offline reinforcement learning lies in the tendency for algorithms to overestimate the value of actions based on limited, static datasets. This occurs because the agent extrapolates from observed data, potentially assigning high values to state-action pairs not adequately represented in the dataset. Consequently, the learned policy may prioritize these overestimated actions, leading to suboptimal performance and instability during deployment. This value overestimation problem isn’t simply a matter of inaccurate prediction; it fundamentally undermines the learning process, creating a positive feedback loop where inflated values reinforce the selection of poor actions. Researchers are actively exploring techniques like conservative Q-learning and uncertainty quantification to mitigate this issue and enhance the reliability of offline RL agents, striving to ensure learned policies reflect genuine, rather than illusory, benefits.

Selective learning curves reveal that optimal performance is achieved with high, medium, or low context encoder learning rates depending on the dataset.
Selective learning curves reveal that optimal performance is achieved with high, medium, or low context encoder learning rates depending on the dataset.

Embracing Uncertainty with Bayesian Reinforcement Learning

Bayesian Reinforcement Learning (RL) addresses uncertainty by representing agent beliefs as probability distributions over possible models of the environment. Unlike frequentist RL which estimates a single optimal policy, Bayesian RL maintains a posterior distribution over policies and value functions, updated with each observed interaction. This is achieved through Bayes’ theorem, combining prior beliefs with likelihoods derived from observed data. When data is limited, the prior distribution significantly influences the agent’s behavior, preventing overconfident estimations and promoting exploration. This probabilistic representation allows for quantifying confidence in the learned policy, providing a mechanism for risk-aware decision-making and improved generalization performance, especially in scenarios with sparse rewards or high stochasticity. The agent effectively learns a distribution over possible solutions, rather than a single point estimate, leading to more robust and adaptable behavior.

Bayesian Reinforcement Learning (RL) addresses the limitations of frequentist RL methods when facing data scarcity or noisy environments by representing agent beliefs as probability distributions over possible models or parameters. This probabilistic modeling allows for improved exploration; rather than converging prematurely on a single, potentially suboptimal policy, the agent maintains a distribution over policies, encouraging continued investigation of less certain areas of the state-action space. Furthermore, the Bayesian approach inherently mitigates overfitting by averaging predictions across multiple models weighted by their posterior probabilities, effectively regularizing the learning process and reducing the impact of biased or limited datasets. The posterior distribution acts as a prior, preventing the agent from assigning excessive confidence to a model learned from a small or unrepresentative sample, leading to more robust and generalizable policies.

Bayesian Reinforcement Learning (BRL) provides a natural framework for addressing Partially Observable Markov Decision Processes (POMDPs) by explicitly representing beliefs about the agent’s state. In POMDPs, the agent does not have direct access to the complete state of the environment, receiving instead observations that are probabilistically related to the underlying state. BRL maintains a probability distribution over possible states, updated using Bayes’ rule as new observations are received. This belief state, rather than a single state estimate, serves as the input to the agent’s policy and value function. Consequently, the agent can effectively reason about uncertainty in its state estimation and make optimal decisions even with incomplete information, mitigating the need for explicit state estimation techniques often required in traditional RL approaches applied to POMDPs.

Adjusting the uncertainty quantile ζ during rollout truncation impacts performance on D4RL locomotion datasets, with further results presented elsewhere.
Adjusting the uncertainty quantile ζ during rollout truncation impacts performance on D4RL locomotion datasets, with further results presented elsewhere.

Neubay: A Bayesian Approach to Offline Reinforcement Learning

Neubay represents a new approach to offline reinforcement learning (RL) predicated on Bayesian principles. Existing offline RL algorithms often struggle with distribution shift and value overestimation when extrapolating from limited, static datasets. Neubay addresses these limitations by explicitly modeling uncertainty in the learned policy and value functions. This is achieved through a Bayesian framework that allows the algorithm to quantify and propagate uncertainty throughout the learning process, leading to more robust and reliable performance when deployed in unseen states. The core innovation lies in its ability to provide principled uncertainty estimates, which are crucial for safe and effective decision-making in offline settings where online interaction with the environment is not possible.

Neubay’s architecture incorporates multiple techniques to enhance both stability and performance. Deep ensembles, consisting of three independently initialized neural networks, are utilized to reduce variance in the learned value function and policy. Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, process sequential data to capture temporal dependencies crucial for effective long-horizon planning. Layer normalization is applied throughout the network to stabilize training and accelerate convergence by normalizing the activations within each layer, mitigating internal covariate shift and allowing for higher learning rates.

Neubay utilizes adaptive long-horizon planning to address challenges in offline reinforcement learning from extended sequential data. This approach mitigates value overestimation, a common issue when extrapolating from limited datasets, by dynamically adjusting the planning horizon during rollouts. Evaluations demonstrate Neubay’s efficacy through rollouts ranging from 64 to 512 steps, allowing the algorithm to effectively learn policies from long-sequence data without succumbing to the instability often associated with long-term predictions. The adaptive nature of the planning horizon allows for efficient exploration of potential future states and actions, improving policy optimization and overall performance in offline settings.

Adjusting the uncertainty quantile ζ significantly impacts performance when truncating rollouts in NeoRL datasets.
Adjusting the uncertainty quantile ζ significantly impacts performance when truncating rollouts in NeoRL datasets.

Validation and Benchmarking with Standard Suites

Neubay has established a strong presence in the field of offline reinforcement learning by achieving state-of-the-art results on several demanding benchmark suites, including D4RL and NeoRL. The system successfully navigated and learned from 7 out of 33 datasets within these suites, indicating a broad applicability and robustness across diverse robotic control tasks. This performance is particularly noteworthy as offline RL algorithms must learn from static datasets without further environmental interaction, posing a significant challenge for generalization and adaptation. The ability of Neubay to excel in this setting suggests its potential for real-world applications where collecting new data is costly or impractical, and pre-collected datasets must be leveraged for effective policy learning.

Evaluations on established locomotion benchmarks reveal Neubay’s strong performance, achieving an average normalized score of 80.1 on the D4RL suite and 64.7 on NeoRL. While Neubay demonstrates competitive results, it currently lags slightly behind the leading baseline algorithms, which attain scores of 83.6 on D4RL and 73.3 on NeoRL. These findings indicate a promising trajectory for Neubay, suggesting that further refinement could bridge the remaining gap and potentially exceed the performance of existing state-of-the-art methods in offline reinforcement learning for locomotion tasks.

The challenging D4RL Adroit benchmark assesses an agent’s ability to manipulate objects with a robotic hand, requiring precise motor control and complex planning. Neubay achieves an average normalized score of 21.1 on this benchmark, indicating a strong level of performance in these demanding tasks. While the current leading model-based approach attains a score of 28.1, Neubay demonstrates competitive capabilities, suggesting its potential for further refinement and optimization in complex manipulation scenarios. This result underscores the effectiveness of the algorithm in addressing high-dimensional action spaces and intricate robotic control problems, positioning it as a promising solution for advanced robotics applications.

The consistently strong results achieved by Neubay underscore the advantages of integrating Bayesian principles with thoughtfully designed algorithmic elements. This combination allows for a more nuanced understanding of uncertainty inherent in offline reinforcement learning, enabling the agent to make more reliable predictions and decisions even with limited or imperfect data. The Bayesian framework facilitates effective exploration and exploitation, while the carefully engineered components optimize performance across diverse and challenging benchmarks. This synergy not only improves the robustness of the learning process but also contributes to a more adaptable and generalizable agent, capable of excelling in complex and dynamic environments.

Adjusting the uncertainty quantile ζ during rollout truncation impacts performance on D4RL locomotion tasks.
Adjusting the uncertainty quantile ζ during rollout truncation impacts performance on D4RL locomotion tasks.

The pursuit of robust intelligence necessitates a mindful simplification of complex systems. Neubay, as presented in this work, embodies this principle by addressing the inherent challenges of long-horizon planning in offline reinforcement learning. The algorithm’s Bayesian approach, leveraging epistemic uncertainty, functions as a crucial refinement – a removal of unnecessary assumptions that plague traditional methods. This echoes Tim Bern-Lee’s sentiment: “The web is more a social creation than a technical one.” Neubay isn’t merely a technical advancement; it’s a refinement of the underlying logic, promoting a clearer, more reliable path toward adaptable, intelligent systems, particularly when faced with imperfect data.

What Lies Ahead?

The pursuit of offline reinforcement learning, as exemplified by this work, continues to circle a fundamental truth: data quality remains the gravitational center of any intelligent system. Neubay demonstrates a skillful navigation of low-fidelity datasets, but it does not erase the need for information. Future efforts must confront the inevitability of imperfect observations, not simply mitigate their immediate effects. The algorithm’s reliance on recurrent neural networks and long-horizon planning, while effective, introduces computational demands. A leaner, more parsimonious approach – one that prioritizes essential state representation and predictive accuracy – will prove more durable.

The temptation to chase ever-longer planning horizons should be tempered. Compounding errors are not solved by simply adding more steps to the calculation. The focus should shift towards robust uncertainty quantification – a system that knows what it does not know, and acts accordingly. A truly elegant solution will not attempt to predict the distant future with precision, but instead to navigate the present with informed caution.

Ultimately, the benchmark of success will not be achieving state-of-the-art performance on curated datasets, but rather, demonstrating reliable behavior in the face of genuine ambiguity. Code should be as self-evident as gravity. Intuition is the best compiler. The field needs fewer elaborate constructions, and more foundational clarity.

Original article: https://arxiv.org/pdf/2512.04341.pdf

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

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2025-12-06 02:32