Deep Reinforcement Learning in Parameterized Action Space (1511.04143v5)

Abstract: Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, to the best of our knowledge no previous work has succeeded at using deep neural networks in structured (parameterized) continuous action spaces. To fill this gap, this paper focuses on learning within the domain of simulated RoboCup soccer, which features a small set of discrete action types, each of which is parameterized with continuous variables. The best learned agent can score goals more reliably than the 2012 RoboCup champion agent. As such, this paper represents a successful extension of deep reinforcement learning to the class of parameterized action space MDPs.

Deep Reinforcement Learning in Parameterized Action Space: An Analytical Overview

This paper explores the application of deep reinforcement learning (DRL) in parameterized action spaces, notably within the Half-Field Offense (HFO) domain of RoboCup soccer. Unlike traditional reinforcement learning approaches managing purely discrete or continuous action spaces, this paper focuses on a hybrid, parameterized action space. This exploration marks a progression in addressing environments with structured continuous action selections, such as those found in the simulated RoboCup soccer domain.

The authors extend the Deep Deterministic Policy Gradients (DDPG) algorithm to adapt it for parameterized action spaces, which present a combination of discrete action choices coupled with associated continuous parameters. One significant modification highlighted is the implementation of bounded action space gradients—a necessary adaptation to stabilize learning in continuous, bounded environments. They apply this modified approach to simulate RoboCup soccer, demonstrating that their agents can outperform the 2012 RoboCup champion in goal-scoring reliability, albeit at a slower pace. These findings suggest that DRL can successfully be applied to parameterized action space Markov Decision Processes (PAMDPs).

Key Contributions

1. Parameterized Action Space in RL: The paper provides a structured exploration of learning in environments where actions are characterized by discrete types but require real-valued parameters. This setting poses challenges as it adds layers of complexity in determining the optimal policy and value function.
2. Bounded Action Space Gradients: To ensure the validity and stability of learning, the authors introduce an "inverting gradients" method. This approach scales gradient updates in proportion to the distance of an action's parameter to its bound, and inverts gradients when suggested updates would push parameters outside their valid range.
3. RoboCup Soccer Task: Using the HFO domain, agents learned from scratch to locate and approach the ball, manipulate it effectively, and score with high reliability against an empty goal. The learned strategies not only demonstrated competitive competence against hand-coded agents but also illustrated the potential of DRL methods in parameterized action spaces.

Implications and Future Work

The practical implications of this research extend beyond robotic soccer, potentially influencing fields that require decision-making in structured environments with complex action spaces, such as autonomous vehicles and robotics. Theoretically, this paper underscores the adaptability and scalability of deep reinforcement learning architectures.

For future advancements, the paper points to applying these techniques in more complex tasks, such as scoring against an opponent goalie, which necessitates strategic collaboration and competition. Furthermore, extending this work to multi-agent systems within the RoboCup environment could potentially lead to emergent cooperative and competitive behaviors.

In conclusion, this paper makes a methodological contribution to the application of deep reinforcement learning in mixed action spaces and demonstrates its effectiveness in achieving proficient agent behavior in a complex robotic domain. The intersection of DRL and parameterized action spaces invites further investigation that could unlock new levels of autonomy and efficiency in robotic and AI systems.