What are the most efficient reinforcement learning tools for real-time decision making?

1 RL frameworks

One of the most essential RL tools is a framework that provides a common interface and functionality for defining, training, and evaluating RL agents. Popular frameworks include TensorFlow Agents, a library that integrates with TensorFlow and offers a variety of algorithms, environments, and utilities; PyTorch Lightning, a framework that simplifies PyTorch code and enables fast and scalable RL experiments; Ray RLlib, a scalable and distributed RL framework that supports multiple algorithms, environments, and languages; and Stable Baselines3, a set of improved implementations of classic and state-of-the-art RL algorithms based on PyTorch.

2 RL environments

An important tool for reinforcement learning is an environment that simulates a problem or task that the agent needs to solve. This environment provides the agent with observations, actions, and rewards, and can range from simple to complex and realistic. Examples of popular RL environments include OpenAI Gym, Unity ML-Agents, CARLA, and Procgen. OpenAI Gym offers a variety of environments from classic control problems to Atari games to robotics tasks. Unity ML-Agents is a toolkit for creating and using 3D environments with realistic physics and graphics for RL. CARLA is an open-source platform that simulates urban driving scenarios for autonomous vehicles. Procgen is a set of procedurally generated environments that test the generalization and robustness of RL agents.

3 RL algorithms

An essential tool in reinforcement learning is an algorithm that guides the agent in learning from experiences and updating the policy or value function. There are various RL algorithms, such as value-based, policy-based, actor-critic, model-based, and model-free. For instance, Deep Q-Network (DQN) is a model-free algorithm that combines Q-learning with deep neural networks to learn value functions for discrete action spaces. Proximal Policy Optimization (PPO) is another model-free algorithm that uses clipped objective functions and multiple epochs to optimize policies for continuous or discrete action spaces. Soft Actor-Critic (SAC) is a model-free algorithm that utilizes entropy regularization and automatic temperature tuning to learn stochastic policies for continuous action spaces. As for Model-Based Interval Estimation (MBIE), it is a model-based algorithm that employs confidence intervals and exploration bonuses to balance exploration and exploitation for discrete action spaces.