The Option-Critic Architecture (1609.05140v2)

Abstract: Temporal abstraction is key to scaling up learning and planning in reinforcement learning. While planning with temporally extended actions is well understood, creating such abstractions autonomously from data has remained challenging. We tackle this problem in the framework of options [Sutton, Precup & Singh, 1999; Precup, 2000]. We derive policy gradient theorems for options and propose a new option-critic architecture capable of learning both the internal policies and the termination conditions of options, in tandem with the policy over options, and without the need to provide any additional rewards or subgoals. Experimental results in both discrete and continuous environments showcase the flexibility and efficiency of the framework.

• The paper introduces a novel framework that uses policy gradients to simultaneously learn intra-option policies and termination functions without external subgoals.
• The paper demonstrates significant performance gains in domains like Four-Rooms, Pinball, and Atari, outperforming traditional RL methods in both discrete and continuous spaces.
• The paper provides evidence that autonomous temporal abstraction enhances scalability and efficiency in reinforcement learning, paving the way for future research in advanced function approximation.

Summary of "The Option-Critic Architecture"

"The Option-Critic Architecture," authored by Pierre-Luc Bacon, Jean Harb, and Doina Precup, addresses the challenge of autonomously creating temporal abstractions within the framework of options in reinforcement learning (RL). The paper introduces a novel architecture that leverages policy gradient methods to learn intra-option policies and termination functions simultaneously, without the need for pre-specified subgoals or external rewards.

Background and Motivation

Temporal abstraction is a crucial aspect of scaling reinforcement learning algorithms to handle complex tasks. Options, as introduced by Sutton et al. (1999), provide a mechanism to define temporally extended actions, enabling more efficient planning and learning. However, the autonomous discovery of such options has been a challenging endeavor, especially in continuous state and action spaces.

Previous approaches largely focused on discovering subgoals and learning policies to achieve them. These methods, while effective in certain scenarios, suffer from scalability issues due to their combinatorial nature. Additionally, learning policies for subgoals can be computationally expensive, sometimes equating to the complexity of solving the entire task.

Core Contributions

The paper provides a comprehensive solution to simultaneously learn intra-option policies, termination functions, and the policy over options using policy gradients. The main contributions are:

1. Intra-Option Policy Gradient Theorem: A gradient formulation allowing the fine-tuning of policies within options based on the expected overall return.
2. Termination Gradient Theorem: A gradient-based method to optimize termination functions, which determines when an option should terminate, contributing to effective temporal abstractions.

These theorems offer a unified framework that works seamlessly in both discrete and continuous state-action spaces. The approach contrasts sharply with previous combinatorial methods, offering significant improvements in scaling up to large domains.

Methodology

The option-critic architecture operates by continually updating its components based on available experiences. It employs a call-and-return execution model where an agent selects an option according to a policy over options, follows the intra-option policy, and terminates based on the learned termination function.

Key to this architecture is the use of policy gradient methods traditionally applied in RL, extended here to handle the complexities introduced by options. The architecture is capable of efficiently learning meaningful temporally extended behaviors, making it applicable to a variety of environments.

Experimental Results

The authors conducted several experiments to validate their approach:

1. Four-Rooms Domain: The option-critic agent outperformed primitive actor-critic and SARSA agents, demonstrating faster adaptation to changes in task goals. The learned options exhibited useful subgoal-like behaviors without explicit subgoal specifications.
2. Pinball Domain: The agent learned options that specialized in navigating a continuous state-space environment, showcasing effective use of temporal abstractions to reach the goal. The results were achieved with a standard setting, affirming the robustness of the methodology.
3. Arcade Learning Environment: Using a deep neural network to approximate the critic, intra-option policies, and termination functions, the approach was tested on several Atari games. The option-critic architecture successfully learned effective policies that surpassed the performance of the original DQN architecture in multiple games, highlighting the scalability and efficacy of the proposed framework.

Implications and Future Directions

The option-critic architecture represents a significant advance in the autonomous learning of temporal abstractions in RL. Its ability to seamlessly integrate with function approximation techniques—such as deep neural networks—opens up new possibilities for applying RL to more complex, high-dimensional tasks.

Speculative Future Developments

Future work may explore several avenues:

• Sparse Initiation Sets: Extending the architecture to learn initiation sets, allowing options to be selectively available based on the state, could enhance efficiency.
• Regularization Techniques: Investigating various regularization approaches to promote desirable properties in learned options, such as sparsity or compositionality.
• Bias-Variance Tradeoff: Addressing the bias in policy gradient estimators for discounted settings while maintaining practical data efficiency.

The paper sets a new standard for learning temporal abstractions and provides a versatile toolkit for enhancing the capabilities of RL agents across a broad spectrum of applications.