In the world of reinforcement learning, the challenge of effectively managing multiple tasks is akin to juggling several balls—each requiring your attention to prevent them from dropping. This blog post will guide you through the process of implementing a software architecture for Multi-task Reinforcement Learning (MRL) with soft modularization. Whether you are a seasoned AI developer or a curious beginner, this guide aims to make the complex world of MRL more accessible.

Environment Setup

Before delving into the code, let’s ensure your environment is ready for implementing this MRL system. Here are the requirements you need to fulfill:

• Python 3
• Pytorch 1.7
• posix_ipc
• tensorboardX
• tabulate
• gym
• MetaWorld (Make sure to check the next section for setup instructions)
• seaborn (for plotting)

Setting Up MetaWorld

Our method is evaluated on MetaWorld, which is constantly evolving. To ensure compatibility, we will utilize our fork of MetaWorld. Follow these commands to clone and install it:

git clone https://github.com/RchalYang/metaworld.git
cd metaworld
pip install -e .

Exploring Our Network Structure

For specific details about the architecture, refer to ModularGatedCascadeCondNet located in torchrl/network/nets.py. This is where the magic happens!

Training Your Model

All logs and snapshots during training will be stored in a designated logging directory, defaulting to .log/EXPERIMENT_NAME. You can customize this directory using the --log_dir argument when you start your experiment.

To initiate training, you can use the following command structures, depending on whether you’re focusing on Conditioned or Fixed Modular Networks:

Command Examples for Modular Networks

MT10-Conditioned Shallow

python starter/mt_para_mtsac_modular_gated_cas.py --config meta_config/mt10/modular_2_2_2_256_reweight_rand.json --id MT10_Conditioned_Modular_Shallow --seed SEED --worker_nums 10 --eval_worker_nums 10

MT10-Fixed Shallow

python starter/mt_para_mtsac_modular_gated_cas.py --config meta_config/mt10/modular_2_2_2_256_reweight.json --id MT10_Fixed_Modular_Shallow --seed SEED --worker_nums 10 --eval_worker_nums 10

MT50-Conditioned Deep

python starter/mt_para_mtsac_modular_gated_cas.py --config meta_config/mt50/modular_4_4_2_128_reweight_rand.json --id MT50_Conditioned_Modular_Deep --seed SEED --worker_nums 50 --eval_worker_nums 50 

Plotting Training Curves

To visualize how your model is performing over time, you can easily plot the training curves using the command below. Customization options are available for different experiments and seeds:

python torchrl/utils/plot_csv.py --id EXPERIMENTS --env_name mt10 --entry mean_success_rate --add_tag POSTFIX_FOR_OUTPUT_FILES --seed SEEDS

Troubleshooting

If you encounter issues during the setup or training process, consider the following troubleshooting strategies:

• Ensure all dependencies are correctly installed and updated to the versions specified.
• Verify that your environment variables are properly configured.
• Check the compatibility of your hardware with the installed libraries, particularly for Pytorch.
• If any errors arise in the code, reviewing log files in the default logging directory might give you clues.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.