[Paper Review] Continuous Neural Algorithmic Planners

Yu He, Petar Veličković+2 more|arXiv (Cornell University)|Nov 29, 2022

Reinforcement Learning in Robotics4 citations

TL;DR

This paper introduces Continuous Neural Algorithmic Planners (CNAP), a method that extends neural algorithmic reasoning to continuous control by discretizing action spaces via binning and employing selective sampling strategies to manage large planning graphs. CNAP achieves superior sample efficiency and performance over model-free baselines like PPO in low-data, high-dimensional MuJoCo environments, demonstrating the feasibility of integrating algorithmic reasoning into complex continuous control tasks.

ABSTRACT

Neural algorithmic reasoning studies the problem of learning algorithms with neural networks, especially with graph architectures. A recent proposal, XLVIN, reaps the benefits of using a graph neural network that simulates the value iteration algorithm in deep reinforcement learning agents. It allows model-free planning without access to privileged information about the environment, which is usually unavailable. However, XLVIN only supports discrete action spaces, and is hence nontrivially applicable to most tasks of real-world interest. We expand XLVIN to continuous action spaces by discretization, and evaluate several selective expansion policies to deal with the large planning graphs. Our proposal, CNAP, demonstrates how neural algorithmic reasoning can make a measurable impact in higher-dimensional continuous control settings, such as MuJoCo, bringing gains in low-data settings and outperforming model-free baselines.

Motivation & Objective

• To extend implicit planning via neural algorithmic reasoning to continuous action spaces, which remain unexplored in prior work.
• To address the scalability challenge of planning graphs in high-dimensional continuous control by introducing selective neighbor sampling strategies.
• To evaluate whether neural algorithmic reasoning can maintain sample efficiency and generalization in complex, real-world control tasks with continuous dynamics.
• To investigate the impact of different sampling strategies on learning stability and performance in large-scale planning graphs.
• To demonstrate the feasibility of applying algorithmic reasoning to high-dimensional continuous control beyond discrete action spaces.

Proposed method

• Discretizes continuous action spaces using uniform binning to enable application of graph neural network-based value iteration.
• Constructs a planning graph by expanding from the current state over a planning horizon, with actions sampled from binned action spaces.
• Employs four neighbor sampling strategies: manual-Gaussian, learned-Gaussian, reuse-policy, and learned-sampling to reduce graph size.
• Uses a message-passing GNN to simulate value iteration on the planning graph, updating state values via the Bellman optimality equation.
• Trains the agent using a policy gradient objective, with the GNN providing value estimates for planning.
• Integrates the GNN as an inductive bias to enable implicit planning without access to privileged environment information.

Experimental results

Research questions

• RQ1 Can neural algorithmic reasoning be effectively extended to continuous control tasks with high-dimensional action spaces?
• RQ2 How do different neighbor sampling strategies impact the scalability and performance of planning graphs in continuous control?
• RQ3 Does the integration of algorithmic reasoning via GNNs improve sample efficiency and generalization in low-data regimes?
• RQ4 How does the performance of CNAP compare to model-free baselines like PPO in complex MuJoCo environments?
• RQ5 What is the impact of parametric versus non-parametric sampling strategies on learning stability and convergence?

Key findings

• CNAP outperforms PPO in low-data settings on Swimmer and HalfCheetah, achieving higher average episodic rewards with faster learning.
• In Humanoid (action dim=17), all CNAP variants outperform PPO, with nonparametric sampling (e.g., manual-Gaussian) showing the fastest knowledge acquisition.
• On HumanoidStandup, CNAP maintains a stable sitting posture, while PPO agents fall quickly, indicating improved robustness despite similar average returns.
• Qualitative analysis shows CNAP agents maintain balance and execute stable locomotion (e.g., walking in Humanoid), whereas PPO agents frequently fall.
• The use of nonparametric sampling strategies (e.g., manual-Gaussian) leads to more stable learning than parametric alternatives, likely due to fewer trainable parameters.
• CNAP demonstrates that neural algorithmic reasoning can be successfully applied to complex, high-dimensional continuous control tasks, extending beyond discrete action spaces.