In the world of artificial intelligence, reinforcement learning is a powerful technique that allows agents to learn from their actions. The MEEECode allows you to reproduce experiments as discussed in the paper titled Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation. This guide will help you set up and run the code with ease.

Installation Instructions

To get started, you’ll need to follow these steps:

1. Install MuJoCo 2.0:
• Place MuJoCo in the following directory: ~/.mujoco/mujoco200.
• Add your license key to ~/.mujoco/mjkey.txt.
2. Install dependencies for the Linux platform by executing the following commands:
• sudo yum install patchelf
• sudo yum install mesa-libGL-devel mesa-libGLU-devel
• sudo yum install mesa-libOSMesa-devel mesa-libOSMesa
• sudo yum install glfw
• sudo yum install mesa-libGL
• sudo yum install openmpi-devel
3. Create a conda environment and install necessary dependencies:
• Run the command: conda create -n your_env_name python=3.6
• Activate your environment with: conda activate your_env_name
• Install CUDA for TensorFlow GPU support: conda install cudatoolkit==10.0.130 and conda install cudnn==7.6.5
• Install the remaining requirements: pip install -r requirements.txt

Usage Instructions

Once you have everything installed, you can proceed to run your experiments. Follow these steps:

• Locate configuration files in the examples/config directory.
• To conduct the experiment on the Humanoid-v2 environment, use the command:

python main.py run_local examples.development --config=examples.config.humanoid.1 --trial-gpus=1

• This command runs the experiment locally using one Nvidia GPU. You can adjust the environment and GPU settings by modifying the corresponding arguments.

Logging Results

Results from your experiments will be stored in a default directory:

log_dir=~ray_meee

You can also specify a different directory for your logs within the examples/config/configuration_files.

Troubleshooting

If you encounter any issues during installation or when running your experiments, here are some troubleshooting tips:

• Ensure that your MuJoCo license key is correctly placed in the designated directory.
• Check whether all dependencies were successfully installed by revisiting the installation commands.
• If you’re having problems with TensorFlow, make sure that the correct version of CUDA is installed.
• Refer to the original MBPO code base at MBPO for further guidance.

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Conclusion

By following this guide, you’ll be well-prepared to reproduce experiments as detailed in the MEEECode framework. With the complexities of reinforcement learning meticulously broken down into manageable steps, you’re now ready to dive into the world of AI research.

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.