Welcome to the exciting world of Offline Reinforcement Learning (RL)! This blog post will guide you through understanding OfflineRL, its algorithms, installation process, and troubleshooting tips. Let’s dive right in!

What is Offline Reinforcement Learning?

Offline Reinforcement Learning, or batch reinforcement learning, allows algorithms to learn from a fixed dataset without requiring interactions with the environment during training. It’s like learning to bike by watching others instead of having to ride yourself! This enables systems to make informed decisions based on pre-collected data.

Overview of Re-implemented Algorithms

OfflineRL includes a range of algorithms that can be broadly classified into Model-free and Model-based methods.

Model-free Methods

• CRR: Critic Regularized Regression. Learn more about it in the paper here.
• CQL: Conservative Q-Learning. Detailed paper here and code is available here.
• PLAS: Latent Action Space for Offline Reinforcement Learning. Check the paper here and the implementation here.
• BCQ: Off-Policy Deep Reinforcement Learning without Exploration. Read more in the paper here and find code here.
• EDAC: Uncertainty-based offline reinforcement learning with diversified Q-ensemble. Paper here and code here.
• MCQ: Mildly conservative Q-learning. Detailed paper here and code here.
• TD3BC: A minimalist approach to offline reinforcement learning. Find the paper here and code here.
• PRDC: Policy Regularization with Dataset Constraint. Paper details here and code here.

Model-based Methods

• BREMEN: Deployment-efficient RL via model-based offline optimization. Paper here and code here.
• COMBO: Conservative Offline Model-Based Policy Optimization. Find the paper here.
• MOPO: Model-Based Offline Policy Optimization. Read the paper here and code is available here.
• MAPLE: Offline Model-based Adaptable Policy Learning. Paper here and code here.
• MOBILE: Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning. Paper here and code here.
• RAMBO: Robust adversarial model-based offline reinforcement learning. For more insights, you can find the paper here and code here.

Installation Process

Installing the OfflineRL package is straightforward! Just follow these steps:

1. Clone the NeoRL repository:

   git clone https://agit.ai/Polixir/neorl.git

   Change directory:

   cd neorl

   Install using pip:

   pip install -e .

2. (Optional) For D4RL support, run:

   pip install git+https://github.com/rail-berkeley/d4rl@master#egg=d4rl

3. Install OfflineRL:

   pip install -e .

Example Usage

To demonstrate the training procedure using different algorithms, consider the following examples:

• Train on HalfCheetah-v3-L-9:

  python examples/train_task.py --algo_name=cql --exp_name=halfcheetah --task HalfCheetah-v3 --task_data_type low --task_train_num 100

• Train on SafetyHalfCheetah:

  python examples/train_task.py --algo_name=mcq --exp_name=SafetyHalfCheetah --task SafetyHalfCheetah

Viewing Experimental Results

We use Aim for logging and visualizing results. Here’s how to set it up:

cd offlinerl_tmp
aim up

Now you can see the results on http://127.0.0.1:43800.

Troubleshooting

If you run into issues, consider the following troubleshooting tips:

• Ensure all dependencies are installed correctly. Double-check the installation steps.
• If the training script fails, review the error messages for hints on what went wrong.
• For issues with Aim, ensure that Aim is running as specified in the viewing section above.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.