RL Shooter is an open-source reinforcement learning environment built on the ViZDoom platform, offering customizable first-person shooter scenarios. It enables AI researchers to train agents using visual inputs, tailored reward functions, and configurable action spaces. The package supports rapid experimentation and benchmarking of deep RL algorithms in FPS settings, with adjustable frame skipping, map layouts, and logging utilities for reproducible research.
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RL Shooter
RL Shooter is an open-source reinforcement learning environment built on the ViZDoom platform, offering customizable first-person shooter scenarios. It enables AI researchers to train agents using visual inputs, tailored reward functions, and configurable action spaces. The package supports rapid experimentation and benchmarking of deep RL algorithms in FPS settings, with adjustable frame skipping, map layouts, and logging utilities for reproducible research.
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What is RL Shooter?
RL Shooter is a Python-based framework that integrates ViZDoom with OpenAI Gym APIs to create a flexible reinforcement learning environment for FPS games. Users can define custom scenarios, maps, and reward structures to train agents on navigation, target detection, and shooting tasks. With configurable observation frames, action spaces, and logging facilities, it supports popular deep RL libraries such as Stable Baselines and RLlib, enabling clear performance tracking and reproducibility across experiments.
Who will use RL Shooter?
- Reinforcement learning researchers
- AI practitioners and developers
- Academic institutions and students
- Game AI developers
- Deep learning enthusiasts
How to use the RL Shooter?
- Step1: Install ViZDoom following official instructions.
- Step2: Clone the RL_Shooter repository from GitHub.
- Step3: Install Python dependencies using pip install -r requirements.txt.
- Step4: Configure scenario settings in config files (maps, rewards, frame skip).
- Step5: Run training scripts (e.g., python train.py) with chosen RL algorithm.
- Step6: Monitor logs and tensorboard for performance metrics.
- Step7: Evaluate trained agents with evaluation scripts.
- Step8: Modify or add custom maps and reward functions as needed.
Platform
- Linux
- Mac
- Windows
RL Shooter's Core Features & Benefits
The Core Features
- Customizable FPS scenarios on ViZDoom
- Visual frame observation streams
- Configurable reward functions
- Adjustable action space definitions
- OpenAI Gym API compatibility
- Frame skipping and FPS control
- Logging and TensorBoard support
The Benefits
- Rapid prototyping of RL agents
- Benchmarking across algorithms
- Reproducible experimental setup
- Flexible environment customization
- Community-driven open source
RL Shooter's Main Use Cases & Applications
- Training deep RL agents for FPS navigation and combat
- Benchmarking new reinforcement learning algorithms
- Teaching RL concepts in academic courses
- Research on visual-based decision making
- Comparative studies between model architectures
FAQs of RL Shooter
RL Shooter Company Information
- Amine Zouitine
RL Shooter Reviews
5/5
RL Shooter's Main Competitors and alternatives?
ViZDoom native Gym environment
DeepMind Lab
Unity ML-Agents
OpenAI Gym Atari environments
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