(⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.
- ai
- deep-reinforcement-learning
- wpa-psk
- handshakes
Repository Index
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A collection of environments for autonomous driving and tactical decision-making tasks
- reinforcement-learning
- autonomous-driving
- gymnasium-environment
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
- reinforcement-learning
- tensorflow
- contextual-bandits
- bandits
Scalable, event-driven, deep-learning-friendly backtesting library
- reinforcement-learning
- deep-reinforcement-learning
- gym-environment
- openai-gym
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
- gym
- pybullet
- reinforcement-learning
- control
A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms
ROS package for dynamic obstacle avoidance for ground robots trained with deep RL
- tensorflow
- ros
- deep-reinforcement-learning
- collision-avoidance
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind
A brain that learns from live experience. System 1 by default: perception, memory, plasticity and action. Optional System 2 for self-observation and slow corrections.
- machine-learning
- embodied-ai
- online-learning
- python
An OpenAI gym wrapper for CARLA simulator
- carla-simulator
- autonomous-driving
- deep-reinforcement-learning
- imitation-learning
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Communicating with godot engine through shared memory
The official code of Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Deep Reinforcement Learning for Financial Trading using Price Trailing @ ICASSP 2019
Build RL environments for any domain from real cases that Browser Use agents mine from the web
Python application to setup and run streaming (contextual) bandit experiments.
- streaming
- bandit
- mab
- cmab
The repository host gives no description.
Workbench for practical machine learning in Ruby.
- machine-learning
- machine-learning-algorithms
- neural-network
- neural-networks