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Multi-action Tangled Program Graphs for Multi-task... | AI Research

Key Takeaways

Multi-action Tangled Program Graphs for Multi-task Reinforcement Learning with Continuous Control introduces a new approach to solving complex, multi-task re...
Over the past few decades, machine learning has been widely used to learn complex tasks.
Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for specific tasks.
To further challenge algorithms, Multi-Task RL (MTRL) environments have been introduced, requiring a single model to learn multiple behaviors.
The Tangled Program Graph (TPG) algorithm is a Genetic Programming (GP) algorithm designed for discrete MTRL environments.

Paper AbstractExpand

Over the past few decades, machine learning has been widely used to learn complex tasks. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for specific tasks. To further challenge algorithms, Multi-Task RL (MTRL) environments have been introduced, requiring a single model to learn multiple behaviors. The Tangled Program Graph (TPG) algorithm is a Genetic Programming (GP) algorithm designed for discrete MTRL environments. Recently, the MAPLE algorithm has been proposed, as another GP algorithm that achieves high results in single task continuous RL environments. A variation of the TPG is proposed alongside MAPLE, named Multi-Action TPG (MATPG) that aggregates MAPLE agents, and creates a control flow to activate them. Initially tested on single task RL environments only, MATPG achieved similar results to MAPLE. In this work, we present a new benchmark based on the MuJoCo Half Cheetah from Gymnasium. This benchmark features five distinct obstacles that are randomly positioned in front of the agent, each of which demands a unique behavior. This benchmark serves as a use case for MATPG, to prove its ability as a GP solution for continuous MTRL environments. Our experiments demonstrate its superiority in this multi-task use case when combined with lexicase selection. Furthermore, we examine the interpretability of the evolved graph, revealing that the decision flow of the model is fully interpretable.

Multi-action Tangled Program Graphs for Multi-task Reinforcement Learning with Continuous Control introduces a new approach to solving complex, multi-task reinforcement learning (MTRL) problems. While traditional reinforcement learning often focuses on mastering a single task, this research explores how a single model can learn to navigate multiple, distinct behaviors within continuous control environments. By adapting the Tangled Program Graph (TPG) algorithm—a method typically used for discrete tasks—the authors aim to create a flexible and interpretable solution for more complex, continuous scenarios.

Bridging Discrete and Continuous Control

The Tangled Program Graph (TPG) is a genetic programming algorithm known for its effectiveness in discrete multi-task environments. To address continuous control, the authors utilize the MAPLE algorithm, which has shown strong performance in single-task continuous reinforcement learning. The researchers developed Multi-Action TPG (MATPG), which functions by aggregating multiple MAPLE agents and establishing a control flow that determines which agent to activate at any given time. This allows the system to manage different behaviors within a single, unified model.

A New Benchmark for Multi-Task Learning

To test the capabilities of MATPG, the authors introduced a new benchmark based on the MuJoCo Half Cheetah environment. In this setup, the agent must navigate five distinct obstacles placed randomly in its path. Each obstacle requires the agent to perform a unique behavior to succeed. This benchmark serves as a rigorous test to determine if the MATPG framework can successfully handle the demands of continuous multi-task reinforcement learning.

Performance and Interpretability

The study demonstrates that MATPG is highly effective in this multi-task environment, particularly when paired with a technique called lexicase selection. Beyond raw performance, the researchers highlighted the model's transparency. Because the system relies on an evolved graph structure, the decision-making process is fully interpretable, allowing users to clearly see how the model determines which behaviors to trigger. This combination of performance and clarity makes MATPG a promising approach for complex reinforcement learning tasks.

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