| Definition | A library that implements the Hierarchical Actor-Critic algorithm for efficient parallel learning of hierarchical policies in complex RL tasks. |
| No of environments | 4 |
The Hierarchical Actor-Critic (HAC) Gym library implements the Hierarchical Actor-Critic algorithm within a gym-compatible environment. HAC accelerates learning in reinforcement learning tasks by breaking down complex problems into smaller subproblems, allowing agents to learn multiple levels of policies in parallel. By simulating optimal lower-level policies during training, HAC enables each level to focus on learning its respective subtask independently, overcoming the instability challenges typical of hierarchical learning. The framework uses two types of hindsight transitions—hindsight action and hindsight goal transitions—to efficiently train agents with sparse rewards. These strategies allow the HAC algorithm to learn hierarchical policies more effectively than traditional methods, especially in tasks with continuous state and action spaces.
This library offers gym environments where hierarchical agents can be trained for sequential decision-making tasks, including grid-world and robotics simulations. It allows for the training of agents with 1, 2, or 3 levels of hierarchy, providing a flexible and scalable platform for experimentation. By incorporating HAC into the gym framework, this library makes it easier for researchers to explore hierarchical reinforcement learning applications in robotics and other complex domains. With its ability to train agents in parallel and manage sparse rewards, HAC Gym significantly improves the efficiency of learning in multi-level decision-making tasks, offering a powerful tool for advancing HRL research.