Learning model-based planning from scratch (1707.06170v1)

Abstract: Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to construct a plan. Here we introduce the "Imagination-based Planner", the first model-based, sequential decision-making agent that can learn to construct, evaluate, and execute plans. Before any action, it can perform a variable number of imagination steps, which involve proposing an imagined action and evaluating it with its model-based imagination. All imagined actions and outcomes are aggregated, iteratively, into a "plan context" which conditions future real and imagined actions. The agent can even decide how to imagine: testing out alternative imagined actions, chaining sequences of actions together, or building a more complex "imagination tree" by navigating flexibly among the previously imagined states using a learned policy. And our agent can learn to plan economically, jointly optimizing for external rewards and computational costs associated with using its imagination. We show that our architecture can learn to solve a challenging continuous control problem, and also learn elaborate planning strategies in a discrete maze-solving task. Our work opens a new direction toward learning the components of a model-based planning system and how to use them.

• The paper introduces the Imagination-based Planner (IBP), a novel agent that learns model-based planning entirely from scratch using sequences of imagination steps.
• Unlike traditional methods, the IBP learns all components of planning, including the environment model and action policy, directly from its experience interacting with the environment.
• Empirical evaluation on continuous control and discrete maze tasks demonstrates the IBP's versatility and ability to adapt planning strategies and generalize beyond training conditions.

Overview of "Learning model-based planning from scratch"

The paper “Learning model-based planning from scratch” presents an innovative approach to constructing model-based planning agents capable of learning from the ground up without predefined strategies. The research introduces the Imagination-based Planner (IBP), a novel agent that autonomously learns how to devise, evaluate, and implement plans using a sequence of imagination steps. This methodology provides the agent with the potential to adapt planning strategies dynamically based on learned experiences from the environment.

Core Contributions

The paper makes several noteworthy contributions:

1. Imagination-based Planning: The IBP employs imagination steps where it evaluates hypothetical actions and their consequences, forming what is termed as an “imagination tree.” This approach diverges from traditional model-based methods by allowing the agent to "imagine" and iteratively refine potential decisions before acting.
2. Learning from Experience: Unlike standard model-based planning that heavily relies on predefined models and heuristics, the IBP learns all components of planning from experience. This includes the model of the environment, policy for sampling actions, and procedures for aggregating outcomes into actionable plans.
3. Optimization for Computational Costs: The IBP system balances external task rewards against computational costs involved in using imagination, adapting how much it imagines based on these costs.
4. Empirical Evaluation: The framework is assessed in both continuous control tasks and discrete maze-solving tasks, showcasing its versatility and the ability to effectively generalize planning strategies across varying types of problems.

Experimental Evaluation

• Continuous Control Task: A key experiment involved a simulated spaceship control task where the IBP was tasked with maneuvering a spaceship to a target location amidst obstacles while optimizing fuel consumption and path efficiency. The experiments demonstrated how different planning strategies, like one-step and multi-step imagination, impacted the agent’s performance. Agents utilizing sophisticated planning trees showed decreased task loss, supporting the agent’s capacity to adaptively optimize its plans based on the complexity of the task and the available imagination steps.
• Discrete Maze Task: In another experiment, the IBP was tested in navigating mazes with multiple potential goals, which required resolving state aliasing—a condition where different states yield the same perceptual outcome. The IBP managed to construct adaptive search strategies, demonstrating the potential to generalize beyond training conditions by discovering efficient routes and decision pathways.

Implications and Future Directions

The implications of this research are significant for the field of artificial intelligence, particularly in decision-making and control systems. The ability of the IBP to autonomously learn to plan by simulating actions and outcomes presents a promising direction for the development of agents in environments where explicit modeling is impractical or impossible.

1. Theoretical Advancement: This research advances the theoretical understanding of how autonomous systems can develop planning capabilities without predefined strategies or models, potentially aligning with other areas like reinforcement learning and cognitive modeling.
2. Practical Applications: In practice, the IBP can be implemented in diverse domains requiring adaptive planning and decision-making, such as robotics, automated navigation systems, and even complex strategy games.
3. Future Research: Future research can extend these concepts to domains where perception is more complex, involving high-dimensional sensory input or where environmental dynamics are less predictable, necessitating more robust models and flexibility in planning.

This paper effectively paves the way for more adaptive and self-sufficient planning systems, emphasizing the power of learning through interaction and imagination to tackle complex decision-making tasks. Further investigation into efficient algorithms for optimizing the balance between computational resource allocation and task performance stands as a promising avenue for continued exploration.