Planning with Expectation Models for Control (2104.08543v1)

Abstract: In model-based reinforcement learning (MBRL), Wan et al. (2019) showed conditions under which the environment model could produce the expectation of the next feature vector rather than the full distribution, or a sample thereof, with no loss in planning performance. Such expectation models are of interest when the environment is stochastic and non-stationary, and the model is approximate, such as when it is learned using function approximation. In these cases a full distribution model may be impractical and a sample model may be either more expensive computationally or of high variance. Wan et al. considered only planning for prediction to evaluate a fixed policy. In this paper, we treat the control case - planning to improve and find a good approximate policy. We prove that planning with an expectation model must update a state-value function, not an action-value function as previously suggested (e.g., Sorg & Singh, 2010). This opens the question of how planning influences action selections. We consider three strategies for this and present general MBRL algorithms for each. We identify the strengths and weaknesses of these algorithms in computational experiments. Our algorithms and experiments are the first to treat MBRL with expectation models in a general setting.

• The paper demonstrates that updating state-value functions with expectation models simplifies planning in stochastic, non-stationary environments.
• It introduces three strategic methods for action selection that balance computational efficiency and planning performance, validated via experiments.
• The study proves that using a linear expectation model is equivalent to a full distribution model, offering practical benefits for scalable AI systems.

Examining the Role of Expectation Models in Model-Based Reinforcement Learning

Introduction to Model-Based Reinforcement Learning (MBRL) with Expectation Models

Model-Based Reinforcement Learning (MBRL) is distinguished by its utilization of an environment model to predict the outcomes of actions in a given state. This predictive ability facilitates planning by allowing the agent to consider potential future states and make more informed decisions without requiring direct interaction with the environment. A significant aspect of this paper is its focus on expectation models within the MBRL paradigm, particularly in the context of stochastic and non-stationary environments where the employment of a full distribution model may be impractical.

Key Contributions

The paper brings to light several imperative contributions to the MBRL field, including:

• The assertion that planning with expectation models in stochastic non-stationary environments should update a state-value function rather than an action-value function, challenging previous conventions.
• The presentation of three strategical methods by which action selection can be influenced when planning with state-value functions, alongside the delineation of the relative strengths and weaknesses of these strategies through a comprehensive computational experiment framework.
• The introduction of a novel proof establishing that planning with a linear expectation model and state-value functions is equivalent to employing a more complex distribution model for planning, thereby simplifying the computational process without sacrificing the quality of decision-making.
• An in-depth discussion on the implications of using expectation models in MBRL, suggesting areas for further investigation such as the type of backup distribution and the degree of function approximation.

Algorithms and Experimental Results

In order to empirically validate the theoretical claims, the paper outlines specific algorithmic implementations that utilize expectation models for MBRL, notably distinguishing between policy updation mechanisms and their computational implications. The algorithms proposed demonstrate a significant improvement in planning and action selection efficiency within stochastic and shifting goal environments.

Implications for Future Research and AI Development

This research illuminates the potential of expectation models to simplify and enhance the planning phase in MBRL, advocating for their advantages in terms of computational efficiency and scalability. Looking ahead, the paper suggests exploratory pathways such as adjusting the backup distribution method, exploring non-linear value function scenarios, and comparing the efficacy of expectation models against sample models in multi-step planning scenarios. Furthermore, it posits that the embrace of expectation models can lead to more robust and adaptable AI systems capable of operating in dynamically changing environments.

Conclusion

The paper "Planning with Expectation Models for Control" contributes a significant theoretical and practical framework to the model-based reinforcement learning domain. Through asserting the necessity to update state-value functions, showcasing planning strategies, and delineating specific algorithms, this research paves the way for more efficient and scalable AI systems. As the quest for more generalized AI continues, the utilization of expectation models as delineated could mark a pivotal stride towards achieving adaptability and robustness in AI operations amidst uncertain and evolving environments.