[Paper] A Reinforcement Learning-Based Model for Mapping and Goal-Directed Navigation Using Multiscale Place Fields

Source: arXiv - 2601.03520v1

Overview

The paper presents a reinforcement‑learning (RL) framework that mimics the brain’s place‑cell system to let robots build and use maps at several spatial resolutions at once. By combining coarse‑grained and fine‑grained “place fields” and a replay‑driven reward signal, the authors show faster learning and shorter navigation paths in simulated, partially observable environments.

Key Contributions

• Multiscale place‑field architecture – parallel layers of place cells operating at different spatial scales, enabling both global guidance and local precision.
• Replay‑based reward propagation – a biologically inspired mechanism that replays high‑value trajectories to update value estimates without extra environment interaction.
• Dynamic scale‑fusion module – an online weighting scheme that blends information from all scales based on current uncertainty and task demands.
• Empirical validation – extensive simulations demonstrate up to 30 % reduction in path length and 2‑3× faster convergence compared with single‑scale baselines.
• Open‑source implementation – the authors release the codebase (Python + PyTorch) and a set of benchmark mazes for reproducibility.

Methodology

1. Environment & Observation Model

   • The robot operates in a 2‑D grid world with obstacles and limited sensor range (simulating partial observability).
   • At each step it receives a binary occupancy vector and its current (noisy) pose.

2. Multiscale Place Fields

   • Three layers of place cells are instantiated: fine (≈0.5 m), medium (≈2 m), coarse (≈5 m).
   • Each cell’s activation follows a Gaussian bump centered on its preferred location; the width matches the layer’s scale.

3. RL Core (Actor‑Critic)

   • The critic estimates a state‑value function using the concatenated activations from all layers.
   • The actor outputs a probability distribution over discrete motion primitives (forward, turn left/right).

4. Replay‑Based Reward Mechanism

   • After reaching a goal, the system performs offline “replay” of the successful trajectory, propagating the received reward backward through the value network.
   • Replay is weighted by the confidence of each place‑field layer, giving more influence to reliable (coarse) representations early in learning.

5. Dynamic Scale Fusion

   • A learned gating network computes a per‑step weighting vector w = (w_fine, w_med, w_coarse).
   • The final value estimate (V(s) = \sum_i w_i , V_i(s)), where (V_i) comes from the i‑th scale’s critic head.
   • The gate adapts as the robot explores, gradually shifting emphasis toward finer scales as uncertainty drops.

6. Training Loop

   • Standard RL loop (collect experience → update actor/critic via policy gradient) interleaved with replay updates after each episode.

Results & Findings

• Faster learning: The replay mechanism alone cuts convergence time by ~30 %, but the biggest boost comes from multiscale fusion.
• Robustness to sensor noise: When observation noise is increased 3×, the multiscale model’s performance degrades only ~5 % versus ~20 % for the fine‑only baseline.
• Ablation studies: Removing replay or dynamic fusion each hurts performance, confirming that both components are essential.

Practical Implications

• Scalable SLAM alternatives: Developers can replace heavyweight SLAM pipelines with a lightweight, RL‑based map that automatically balances global planning and local obstacle avoidance.
• Fast adaptation in changing environments: Because replay updates value estimates without re‑exploring, a robot can quickly re‑plan after a layout change (e.g., a newly blocked corridor).
• Edge‑friendly deployment: The model runs on a single CPU core (~2 ms per decision) and fits in <10 MB of RAM, making it suitable for embedded platforms (e.g., TurtleBot, DJI RoboMaster).
• Transfer to real‑world robots: The multiscale representation mirrors how mammals navigate, suggesting smoother sim‑to‑real transfer when combined with domain randomization.
• Potential for hierarchical RL: The scale‑fusion gating can be repurposed as a high‑level policy selector, opening doors to more complex tasks like multi‑room delivery or warehouse picking.

Limitations & Future Work

• Simulation‑only validation: Experiments are confined to 2‑D grid worlds; real‑world sensor noise, dynamics, and 3‑D terrain may expose new challenges.
• Fixed number of scales: The current architecture uses three pre‑defined scales; an adaptive mechanism that adds/removes scales on the fly could improve memory efficiency.
• Replay cost: While replay accelerates learning, it adds a computational burst after each episode, which may be problematic for real‑time continuous operation.
• Future directions suggested by the authors include:
  1. Extending the model to continuous action spaces.
  2. Integrating visual landmarks as additional place‑field cues.
  3. Testing on physical robots in dynamic indoor environments.

Authors

• Bekarys Dukenbaev
• Andrew Gerstenslager
• Alexander Johnson
• Ali A. Minai

Paper Information

• arXiv ID: 2601.03520v1
• Categories: cs.NE, cs.AI, cs.RO
• Published: January 7, 2026
• PDF: Download PDF