Welcome to the world of deep reinforcement learning! This article will guide you through the implementation of Synchronous Advantage Actor Critic (A2C) using TensorFlow. A2C, an easier variant of the advantage actor critic method introduced by OpenAI, simplifies many aspects of the original implementation, making it user-friendly and modular. Ready to dive in? Let’s go!

What’s New Compared to OpenAI Baselines?

• Support for TensorBoard visualization per agent running in an environment.
• Easily customizable policy networks.
• Support for diverse environments beyond OpenAI Gym.
• Simple video generation of agent actions in the environment.
• Modular and easy-to-understand code for quick experimentation.

Understanding Asynchronous vs. Synchronous A2C

To grasp the functioning of A2C better, think of a group of chefs in a kitchen preparing a large feast. In the asynchronous version (A3C), each chef operates independently, using his or her recipe. They occasionally share updates on their dishes, leading to a diverse menu–each dish slightly different based on different cooking experiences. In contrast, in the synchronous version (A2C), all chefs communicate their updates simultaneously before adjusting their recipes together. This collaborative approach ensures a more unified flavor throughout the meal while still encouraging each chef to add their own twist!

Supported Environments

This A2C implementation supports various environments, not just those provided by OpenAI Gym. To add a new environment, inherit from the BaseEnv class in envs/base_env.py. You’ll need to implement the following methods:

• make(): Creates the environment and returns a reference to it.
• step(): Executes one step in the environment and returns (observation, reward, done status, additional info).
• reset(): Resets the environment to its initial state.
• get_observation_space(): Returns the shape of the observation space.
• get_action_space(): Returns the number of possible actions.
• render(): Renders the environment visually if appropriate.

Supported Policy Networks

This implementation uses the basic CNN policy network from OpenAI. For custom networks, inherit from BasePolicy in models/base_policy.py and implement the required methods. Update the policy network class name in models/model.py to ensure everything works smoothly.

TensorBoard Visualization

The implementation includes visualization features with TensorBoard, allowing you to view time plots for episode length and total rewards per agent. To launch TensorBoard, run:

tensorboard --logdir=experiments/my_experiment/summaries

Video Generation

Capture the learning process with video generation! Modify the configuration file to set record_video_every to the desired frequency of video generation during training. Videos will automatically be created during testing if the monitor method is implemented in your environment.

Usage

Main Dependencies

• Python 3 or above
• TensorFlow 1.3.0
• Numpy 1.13.1
• Gym 0.9.2
• TQDM 4.15.0
• Bunch 1.0.1
• Matplotlib 2.0.2
• Pillow 4.2.1

Running the Code

To run the implementation, you’ll need to use a configuration file, such as test.json. You can easily create your own configurations tailored to your training/testing needs. Launch the program with:

python main.py config/test.json

Results

Model	Game	Average Score	Max Score
CNNPolicy	Pong	17	21
CNNPolicy	Breakout	650	850

Troubleshooting Tips

If you encounter any issues while implementing A2C, consider these troubleshooting ideas:

• Ensure all dependencies are correctly installed and versions are compatible.
• Verify the environment is set up correctly in A2C.py.
• Consult TensorBoard logs for insights into training progress.
• If videos aren’t generated, check the monitor method implementation.
• For further assistance, reach out to the community or check the project documentation.

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