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Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
Boyi Liu, Lujia Wang, Ming Liu
TL;DR
Robots need to retain and transfer experience so they can learn efficiently in new environments and use prior knowledge. The paper proposes LFRL, a cloud-based lifelong federated architecture with knowledge fusion and transfer learning, and reports improved navigation-learning efficiency and prior-knowledge use. The authors also describe a deployment website and a fixed input/output-dimension requirement as a scope boundary.
Problem
Robot navigation reinforcement learning still faces challenges in reducing training time, retaining data over long periods, separating computation, and adapting rapidly to new environments.
Method
LFRL combines cloud robotic navigation, a knowledge fusion algorithm for evolving shared models, and transfer-learning approaches for new environments.
Results
Experiments and deployment show that LFRL reduces training time, uses prior knowledge, adapts quickly to new environments, and fuses private models into an evolving shared model.
Takeaways & Limitations
LFRL provides a cloud robotic architecture for lifelong navigation learning with asynchronous shared-model evolution and a released navigation-learning service.
Abstract
from arXiv · showhide
This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL.
I. INTRODUCTION
The paper introduces LFRL to help cloud-connected robots learn efficiently in new environments while retaining and using prior experience. Its architecture combines knowledge fusion, transfer learning, and a cloud navigation-learning service.
- Reinforcement learning supports reactive navigation and can improve real-time performance and adaptability, but navigation applications still face training-time, long-term storage, computation, and rapid-adaptation challenges.
- LFRL is a lifelong reinforcement-learning architecture for navigation in cloud robotic systems, inspired by human cognitive science.
- Experiments in public and self-made environments indicate that LFRL enables robots to use prior knowledge and adapt quickly to new environments.
- Its knowledge fusion algorithm combines robots’ prior knowledge and evolves a shared cloud model.
- Two transfer-learning approaches are introduced to help robots adapt quickly to new environments.
- A cloud robotic navigation-learning website was released to provide an LFRL-based service.
II. RELATED THEORY
The related theory connects robot navigation with deep reinforcement learning, lifelong learning, and federated learning. These areas address reactive navigation, knowledge retention and transfer, and distributed model training without centralizing raw data.
- Deep reinforcement learning has been applied to navigation directly from raw sensor inputs, including target-driven navigation, policy transfer, auxiliary tasks, SLAM-like structures, and multi-robot collision avoidance.
- B. Lifelong machine learning: Lifelong machine learning studies systems that learn multiple tasks or domains over time and selectively transfer stored knowledge to new tasks.
- B. Lifelong machine learning: Effective lifelong learning requires retaining knowledge across tasks and selectively using it to solve new tasks efficiently.
- B. Lifelong machine learning: Prior lifelong-learning work includes shared representations, distillation, latent bases, differentiable transfer weights, temporal skill transfer, and task-to-task reuse.
- Federated learning trains models from distributed data while keeping raw data at multiple edge nodes instead of sending it to a central location.
D. Cloud robotic system
LFRL is a cloud-robotic navigation architecture that combines local reinforcement learning, cloud knowledge fusion, and transfer back to robots. Its shared model evolves through private models from robots and environments, supporting lifelong learning and adaptation.
- D. Cloud robotic system: LFRL combines a cloud server, environments, and robots to support lifelong and multi-robot federated reinforcement learning for navigation.The architecture stores and fuses navigation knowledge across robots and environments.
- D. Cloud robotic system: Robots learn new obstacle-avoidance skills locally, upload private Q-network models, and contribute them to the cloud shared model.Private models may come from one robot training in different environments or from multiple robots.
- D. Cloud robotic system: Shared-model knowledge returns to robots through successor features or parameter transfer, giving new Q-networks an informed initialization for unknown environments.The architecture iterates cloud-to-robot transfer as the cloud model becomes increasingly powerful.
- D. Cloud robotic system: Unlike A3C and UNREAL, LFRL supports asynchronous model evolution across variable environments and agents with potentially different hidden-layer structures.A3C and UNREAL fuse parameters while training under a constant environment and require matching network structures.
- D. Cloud robotic system: The cloud fuses private models into an evolving shared model, allowing navigation skills for multiple obstacle types to accumulate over time.This continuous evolution implements a lifelong-learning pattern and provides knowledge storage and fusion across environments.
- D. Cloud robotic system: The processing algorithm sends private-network parameters to the cloud and evolves the shared model when the configured evolution condition is true.The cloud may fuse models at a fixed frequency rather than after every received private network.
B. Knowledge fusion algorithm in cloud
LFRL fuses private networks and the cloud’s shared model into an evolved shared network using confidence-weighted labels. The cloud model remains a cautious pre-trained model or feature extractor rather than each robot’s final policy.
- Knowledge fusion: The cloud knowledge fusion algorithm generates a new shared model from private robot models and the existing shared model.The generated model becomes the evolved shared network.
- Knowledge fusion: Training samples use sensor data, target data, and human-defined features, with labels formed from confidence-weighted actor scores.The resulting network is trained to fit the generated sample labels.
- Confidence weighting: Information entropy defines confidence because it better represents uncertainty than variance under the paper’s stated conditions.The algorithm weights actors according to their score distributions for each sample.
- Confidence weighting: Changing confidence values produce different actor weights across samples, allowing the generated network to adjust dynamically to sensor data.The paper gives nearly 50,000 confidence combinations for 50,000 generated samples as an example.
- Deployment scope: The cloud shared model is used as a pre-trained model or feature extractor, not as the local robot’s final policy.It is described as cautious rather than optimal for every private environment.
C. Transfer the shared model
LFRL transfers the cloud shared model to local navigation through two approaches: initializing the actor network or using the shared model as a feature extractor. These approaches target obstacle avoidance and target reaching while accommodating different network structures and sensor types.
- Transfer approaches: The two transfer approaches use the shared model as either an initial actor network or a feature extractor.The first preserves existing navigation abilities initially, while the second supplies additional features.
- Initial actor network: Using the shared model as an initial actor network can improve the robot’s final score, but may require altered training settings.Suggested adjustments include faster updating, increased punishment, and lower random-action probability.
- Feature extractor: Using the shared model as a feature extractor increases feature dimensionality and can improve transfer effects stably.Transferred features are inputs to private networks, not to the shared network.
- Feature extractor: The feature-extractor approach keeps the private input layer aligned with the original feature vector and remains applicable across differing network structures.This design addresses structural differences between shared and private input layers.
- Sensor-specific transfer: For image sensors, the paper recommends convolutional-layer outputs as features; for non-image sensors, it uses the output of the entire network.The recommendation depends on whether the Q-network is convolutional.
D. Explanation from human cognitive science
LFRL models robot learning on human decision-making: personal and observed experience jointly influence decisions, while clearer judgments receive greater weight. Knowledge fusion and transfer learning correspond to evolving and reusing that experience.
- Cognitive analogy: The architecture treats a decision model’s evolving policy as analogous to a chess player’s changing level through experience.This analogy motivates lifelong learning in LFRL.
- Cognitive analogy: LFRL parallels human decision-making by combining a robot’s own experience with experience learned from other robots.The paper maps this combination to knowledge fusion and later transfer learning.
- Adaptive weighting: Experiences with clearer judgments receive higher decision weight, which LFRL models through adaptive weighting of training-data labels.The influence of each experience can vary dynamically at each decision step.
IV. EXPERIMENTS
The experiments are designed to evaluate whether LFRL reduces training time without sacrificing navigation accuracy, whether knowledge fusion improves the shared model, and whether transfer methods work for specific tasks.
- Experimental questions: The experiments compare a generic approach with LFRL to assess training time and navigation accuracy.The first question asks whether efficiency improves without sacrificing accuracy.
- Experimental questions: A separate experiment evaluates whether the knowledge fusion algorithm increases the effectiveness of the shared model.This addresses the paper’s second experimental question.
- Experimental questions: The experiments also test whether transfer learning approaches successfully transfer the shared model to specific tasks.This addresses the paper’s third experimental question.
A. Experimental setup
The experiments use four simulated Gazebo environments with increasing obstacle complexity and compare generic training from scratch with LFRL. A Turtlebot3 with a laser range sensor and a two-layer actor-critic network serves as the platform and model.
- A. Experimental setup: Four Gazebo environments progress from walls only to static, moving, and more complex static obstacles.Env-1 has no obstacles except walls; Env-2 has static cylinders; Env-3 has moving cylinders; Env-4 has more complex static obstacles.
- A. Experimental setup: A Turtlebot3 equipped with a laser range sensor is used in every environment.The scanning range is 0.13m to 4m.
- A. Experimental setup: The comparison contrasts generic training from scratch with LFRL across the four training environments.Figure 6 presents the training environments and compares generic and LFRL scores during training.
- A. Experimental setup: The actor-critic network uses two fully connected layers with 64 units, followed by linear outputs for action probabilities and value estimation.The action output uses softmax over discrete action probabilities.
B. Evaluation for the architecture
LFRL trains navigation policies through successive shared-model downloads and uploads across four environments. Compared with generic methods, it improves navigation-decision accuracy and reduces training time in Env-2 through Env-4.
- B. Evaluation for the architecture: LFRL evolves shared models from 1G through 4G as robots train privately and upload models for cloud fusion.After each environment, the cloud fuses the private model with the current shared model before the next evolution.
- B. Evaluation for the architecture: In Env-2 through Env-4, LFRL increases navigation-decision accuracy and reduces training time.The reported improvement is more efficient with the shared model.
- B. Evaluation for the architecture: LFRL is reported as highly effective for learning a policy across the considered obstacles.The evaluation also reports improved generalization across commonly encountered settings.
C. Evaluation for the knowledge fusion algorithm
The knowledge-fusion evaluation tests LFRL in three new environments resembling real situations and compares shared-model rewards with generic models. The shared model steadily reduces training time and performs well across multiple environments.
- C. Evaluation for the knowledge fusion algorithm: Three new testing environments include static obstacles, moving obstacles, and combined complex static and moving obstacles.Test-Env-1 uses cardboard boxes, dustbins, and cans; Test-Env-2 uses moving stakes; Test-Env-3 combines complex obstacle types.
- C. Evaluation for the knowledge fusion algorithm: The shared model steadily reduces training time compared with generic models.Figure 7 presents the compared results, while Table 1 provides quantitative results.
- C. Evaluation for the knowledge fusion algorithm: Generic models make excellent decisions only in individual environments, whereas the shared model makes excellent decisions in multiple environments.The paper attributes this comparison to the effectiveness of the proposed knowledge-fusion algorithm.
- C. Evaluation for the knowledge fusion algorithm: In the later training stage, shared-model rewards account for a larger proportion of positive rewards.Figure 7 uses stacked plots for the comparison across testing environments.
- C. Evaluation for the knowledge fusion algorithm: The table compares results across models, with darker cell backgrounds indicating better performance.The supplied table caption identifies the display as a contrast experiment.
E. Real world experiments
The real-world experiment transfers reinforcement learning from Env-4 to a Turtlebot navigating an indoor office. The reported experiment indicates that the resulting policy is reliable in the real environment, while the architecture retains fixed input and action dimensions.
- E. Real world experiments: The Turtlebot navigates automatically in an indoor office environment using the transferred policy.The policy was obtained by transferring reinforcement learning in Env-4.
- E. Real world experiments: The experiment indicates that the policy is reliable in the real environment.The paper notes that reference corroborates this conclusion.
- E. Real world experiments: LFRL was validated in policy-learning experiments and accompanied by a website providing the cloud robotic navigation-learning service.The conclusion also describes model fusion and asynchronous shared-model evolution.
- E. Real world experiments: The architecture requires fixed dimensions for input sensor signals and actions.Making LFRL flexible to different input and output dimensions is left as future work.