keras-rl/keras-rl
Keras Rl
keras-rl is a reinforcement learning library that enables the training of neural agents using Keras. It serves as a framework for implementing deep reinforcement learning agents that interact with simulated environments to discover optimal behaviors and maximize cumulative rewards.
The library provides a system for configuring, training, and managing neural network agents. It handles the interaction loop between agents and environments, allowing models to learn through direct experience and gradient-based optimization.
The framework includes capabilities for model weight management, allowing users to save and restore the learned states of trained agents to preserve progress or deploy them for evaluation.
Features
- Deep Reinforcement Learning Implementations - A comprehensive framework for implementing deep reinforcement learning agents using neural networks.
- Agent-Environment Interaction Loops - Provides the core interaction loop that generates training data through agent-environment experience.
- Keras Model Implementations - Utilizes the Keras API to define and train the neural networks that serve as agent function approximators.
- RL Agent Managers - Manages the full lifecycle of neural agents, from configuration and training to weight saving.
- Reinforcement Learning - A specialized library for implementing reinforcement learning agents using Keras neural networks.
- Reinforcement Learning Training - Provides a general framework for training agents in simulation environments using stochastic gradient descent.
- RL Agent Implementation Frameworks - Provides the structural support and abstractions needed to implement reinforcement learning agents.
- Simulation Environment Interfacing - Connects deep learning models to environments where they can take actions and receive rewards.
- Agent Training Workflows - Coordinates the configuration and execution of learning algorithms to teach agents optimal behaviors.
- Model Weight Management - Provides utilities for storing and loading model weights to preserve agent progress.
- Experience-Driven Weight Updates - Updates neural network weights based on the outcomes of actions taken within a simulated environment.
- Agent Training Configurations - Prepares agents for training by configuring the necessary optimizers and evaluation metrics.
- Training Parameter Configurations - Provides a system to configure optimizers and performance metrics independently from the learning algorithm logic.
- Model State Serialization - Enables saving and restoring of neural network weights and optimizer states to preserve training progress.
- Deep Learning Ecosystems - Deep reinforcement learning for Keras.
- Deep Learning Frameworks - Deep reinforcement learning for Keras.
- Frameworks de Machine Learning - Reinforcement learning for Keras.
- Reinforcement Learning - Deep reinforcement learning for Keras.
