Highway Graph to Accelerate Reinforcement Learning

• URL: http://arxiv.org/abs/2405.11727v1
• Date: Mon, 20 May 2024 02:09:07 GMT
• Title: Highway Graph to Accelerate Reinforcement Learning
• Authors: Zidu Yin, Zhen Zhang, Dong Gong, Stefano V. Albrecht, Javen Q. Shi,
• Abstract summary: We propose a novel graph structure, named highway graph, to model the state transition. By integrating the highway graph into RL, the RL training can be remarkably accelerated in the early stages. Deep neural network-based agent is trained using the highway graph, resulting in better generalization and lower storage costs.
• Score: 18.849312069946993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning (RL) algorithms often suffer from low training efficiency. A strategy to mitigate this issue is to incorporate a model-based planning algorithm, such as Monte Carlo Tree Search (MCTS) or Value Iteration (VI), into the environmental model. The major limitation of VI is the need to iterate over a large tensor. These still lead to intensive computations. We focus on improving the training efficiency of RL algorithms by improving the efficiency of the value learning process. For the deterministic environments with discrete state and action spaces, a non-branching sequence of transitions moves the agent without deviating from intermediate states, which we call a highway. On such non-branching highways, the value-updating process can be merged as a one-step process instead of iterating the value step-by-step. Based on this observation, we propose a novel graph structure, named highway graph, to model the state transition. Our highway graph compresses the transition model into a concise graph, where edges can represent multiple state transitions to support value propagation across multiple time steps in each iteration. We thus can obtain a more efficient value learning approach by facilitating the VI algorithm on highway graphs. By integrating the highway graph into RL (as a model-based off-policy RL method), the RL training can be remarkably accelerated in the early stages (within 1 million frames). Comparison against various baselines on four categories of environments reveals that our method outperforms both representative and novel model-free and model-based RL algorithms, demonstrating 10 to more than 150 times more efficiency while maintaining an equal or superior expected return, as confirmed by carefully conducted analyses. Moreover, a deep neural network-based agent is trained using the highway graph, resulting in better generalization and lower storage costs.

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