DeNA HandyRL
  • 26-Feb-2023
Lightrun Team
Author Lightrun Team
DeNA HandyRL

BrokenPipeError: [Errno 32] Broken pipe in DeNA HandyRL

Lightrun Team
Lightrun Team

Explanation of the problem

During the training process, the program encountered an exception and terminated abruptly on a server while training to epoch 189. The error message suggests that there was a broken pipe during communication between different threads, causing the program to fail. However, the same configuration was tested on a personal computer and did not encounter any issues.

The error messages displayed when the program failed include information about the location of the error, the thread in which the error occurred, and the type of error encountered. The specific error messages include “BrokenPipeError” and “EOFError”. These error messages provide important information that can be used to diagnose and fix the issue.

The code blocks provided include YAML configuration information for the training and worker processes. This information includes settings such as the observation type, gamma value, batch size, and target policy. Understanding these settings is crucial for reproducing and troubleshooting the issue that caused the program to fail.

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Problem solution for BrokenPipeError: [Errno 32] Broken pipe in DeNA HandyRL

Based on the provided information, the error message indicates that there was a communication problem between the sender and receiver threads in a DeNA HandyRL program. The error occurred during training, specifically after training for 189 epochs, on a server. The training was interrupted, but the same configuration worked fine on the user’s own computer.

The error message includes a traceback that provides information about the exception that occurred in each of the three threads. Thread-4 encountered a “BrokenPipeError: [Errno 32] Broken pipe” exception, while Thread-5 and Thread-6 both encountered “EOFError” exceptions. The error messages suggest that there was a problem with the communication channels between the threads.

In addition to the error message, the provided information includes the YAML configuration for the training and worker arguments. This information may be useful for further debugging or troubleshooting.

To solve this problem, it may be necessary to investigate the communication channels between the sender and receiver threads in the program. Possible solutions may include checking network connections, debugging the code that handles the communication, or adjusting the configuration settings for the training and worker arguments.

Other popular problems with DeNA HandyRL

Problem: Memory errors when using large batch sizes

When using large batch sizes in DeNA HandyRL, you may encounter memory errors that result in crashes or failed runs. This is because the default implementation of the replay buffer, which is used to store and sample past experiences, can become overwhelmed with large amounts of data.


To solve this problem, you can use a more efficient implementation of the replay buffer, such as prioritized experience replay, which prioritizes more informative experiences over less informative ones. This reduces the overall size of the replay buffer and can help prevent memory errors when using large batch sizes.

Problem: Slow convergence and training instability

Another common problem with DeNA HandyRL is slow convergence and training instability, which can result in long training times and suboptimal performance. This problem is often caused by a combination of factors, including poor network architecture design, lack of regularization, and inefficient optimization algorithms.


To solve this problem, you can try several techniques, such as adjusting the network architecture, adding regularization techniques like dropout or weight decay, and using more efficient optimization algorithms like Adam or RMSProp. Additionally, monitoring training progress through metrics like loss and accuracy can help you identify and diagnose convergence issues early on.

Problem: Difficulty tuning hyperparameters

Finally, tuning hyperparameters can be a challenging and time-consuming process in DeNA HandyRL, especially when dealing with complex deep reinforcement learning algorithms. This is because the optimal hyperparameters depend on several factors, including the specific environment and the model architecture used.


To solve this problem, you can use automated hyperparameter tuning tools, such as Hyperopt or Optuna, which use advanced search algorithms like Bayesian optimization or random search to efficiently explore the hyperparameter space and identify optimal configurations. Additionally, using techniques like cross-validation and early stopping can help you validate hyperparameter choices and prevent overfitting.

A brief introduction to DeNA HandyRL

DeNA HandyRL is a popular open-source reinforcement learning library designed for mobile devices. The library is built on top of TensorFlow and provides an easy-to-use interface for developing reinforcement learning algorithms on mobile devices. The library supports a wide range of algorithms such as DQN, PPO, and A2C, and also includes several pre-trained models for various tasks such as Atari games and robotics. One of the key features of the library is its efficient implementation, which allows it to run on mobile devices with limited computational resources. This makes it an ideal choice for developing reinforcement learning-based applications on mobile devices.

The library provides a comprehensive set of tools for developing and evaluating reinforcement learning algorithms. These include utilities for data preprocessing, training and evaluation of models, and visualization of results. The library also supports multi-agent reinforcement learning, which is useful for developing complex applications that require multiple agents to interact with each other. In addition, the library provides several pre-trained models, which can be used as a starting point for developing custom models for specific tasks. Overall, DeNA HandyRL is a powerful and versatile reinforcement learning library that is well-suited for developing cutting-edge mobile applications.

Most popular use cases for DeNA HandyRL

  1. Training and testing RL algorithms: DeNA HandyRL provides a simple and flexible framework for designing and testing RL algorithms. It comes with a variety of built-in environments, such as Atari games and OpenAI Gym, that can be used to evaluate the performance of different RL algorithms. Additionally, it provides a range of RL algorithms that can be easily customized and extended to fit specific use cases.
  2. Developing RL-based applications: DeNA HandyRL can also be used to develop RL-based applications, such as autonomous agents or game AI. For example, it can be used to train a game-playing agent to compete against human players or to develop an autonomous agent that can navigate complex environments.
  3. Customizing and extending the library: DeNA HandyRL is open source and can be easily customized and extended to fit specific use cases. It provides a wide range of APIs and code blocks that can be used to build custom RL algorithms, modify existing algorithms, or add new features to the library. For example, users can extend the library to add new environments or to customize the reward functions used in the RL algorithms.

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