Adept is a powerful reinforcement learning framework designed to help researchers accelerate their work by removing engineering hurdles typically associated with deep reinforcement learning. With features like multi-GPU training, a modular interface, and baseline reinforcement learning algorithms implemented in PyTorch, Adept is an invaluable tool for AI enthusiasts. In this article, we’ll walk you through the installation process, quickstart instructions, and some troubleshooting ideas.
Installation
Getting Adept up and running is a breeze! Just follow the instructions below:
bash
git clone https://github.com/heronsystems/adeptRLcd
cd adeptRL
pip install -e .[all]
If you prefer to use Docker, you can follow the docker instructions.
Quickstart: Training an Agent
To get started with training an agent, Adept provides easy commands to use both local and distributed modes. Here’s how:
- Local Mode (A2C): We recommend using a system with at least 4GB GPU memory, 8GB RAM, and 4 CPU cores. Run:
bash
python -m adept.app local --env BeamRiderNoFrameskip-v4
bash
python -m adept.app distrib --env BeamRiderNoFrameskip-v4
bash
python -m adept.app actorlearner --env BeamRiderNoFrameskip-v4
bash
python -m adept.app -h
Creating Custom Implementations
If you want to use your own Agent, Network, Environment, or SubModule, the structure is straightforward:
python
from adept.scripts.local import parse_args, main
from adept.network import NetworkModule, SubModule1D
from adept.agent import AgentModule
from adept.env import EnvModule
class MyAgent(AgentModule):
pass # Implement
class MyEnv(EnvModule):
pass # Implement
class MyNet(NetworkModule):
pass # Implement
class MySubModule1D(SubModule1D):
pass # Implement
if __name__ == '__main__':
import adept
adept.register_agent(MyAgent)
adept.register_env(MyEnv)
adept.register_network(MyNet)
adept.register_submodule(MySubModule1D)
main(parse_args())
Run your script by specifying the agent and environment:
bash
python my_script.py --agent MyAgent --env env-id-1 --custom-network MyNet
Features of Adept
Adept offers a myriad of features to enhance your deep reinforcement learning journey, including:
- Multi-GPU support with NCCL backend for efficient training
- Modular Network Interface to support different shapes with stateful networks
- OpenAI Gym environments for benchmarking
- Baseline algorithms like Advantage Actor Critic (A2C) and IMPALA
Performance Insights
Adept showcases remarkable performance, achieving up to 12,000 frames per second on Atari with a GeForce 2080 Ti. Our experiments demonstrated:
- Training over 200 million frames in under an hour
- Efficiency with NVLINK and InfiniBand support for multi-GPU environments
Troubleshooting Guide
Encountering issues? Here are some common troubleshooting ideas to assist you:
- Ensure your GPU is set up correctly and has enough memory.
- Check dependencies and ensure you have the required packages installed.
- Consult the logs located in
tmp/adept_logs
for any error messages. - If you encounter any performance issues, consider optimizing your hyperparameters based on the provided defaults.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Final Thoughts
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.
Now that you know how to get started with Adept, dive into your deep reinforcement learning projects and push the boundaries of what’s possible!