Training Tool for Multiagent Scenarios with Reinforcement Learning in Unreal Engine
Discover how Unray can power your game and simulation development with reinforcement learning in Unreal Engine:
1. Interactive Game Development: Use Unray to create complex game environments with multiple agents that learn and adapt as they play.
2. Realistic Environment Simulations: Create realistic simulations to train agents in environments that mimic real-world situations.
3. Research in Artificial Intelligence: Employ Unray as a research platform to experiment with different reinforcement learning algorithms in multi-agent environments.
- Uses powerful RLlib technology for effective training.
- Leverages the ability to parallelize training using Ray technology.
- Supports a variety of algorithms, including PPO, QMIX, DQN, in addition to those built into the RLLib library.
- Facilitates the creation of multi-agent environments.
Demo Video: https://youtu.be/6lu0gTPYFzY
Features: (Please include a full, comprehensive list of the features of the product)
Number of Blueprints: 9
Number of C++ Classes: 1
Network Replicated: No
Supported Development Platforms: Windows
Important/Additional Notes: Unray plugin is complimented with a Python API counterpart, which makes use of RLLib, so it is necessary to install and develop the training from a Python IDE.