#Actor-Critic

The project provides modular implementations of essential deep reinforcement learning algorithms using Keras suitable for discrete and continuous action spaces. It features Actor-Critic approaches like A2C and A3C and Deep Q-Learning variations such as DDQN with prioritized experience replay and dueling networks. Requiring Keras 2.1.6 and OpenAI Gym, it facilitates efficient and scalable setups, with tools for visualization and monitoring through TensorBoard and Plotly, focusing on stability and exploration in complex environments.

Discover the latest in Deep Reinforcement Learning (DRL) through this detailed guide, covering foundational concepts, unsupervised techniques, multi-agent frameworks, and the fusion of policy and value-based approaches. Updated with recent innovations like HILP and EDDICT, this resource provides essential insights into RL framework progressions and applications. It offers a balanced overview of current research and practical applications without unnecessary embellishments.