Self-Imitation Learning (1806.05635v1)

Abstract: This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions. This algorithm is designed to verify our hypothesis that exploiting past good experiences can indirectly drive deep exploration. Our empirical results show that SIL significantly improves advantage actor-critic (A2C) on several hard exploration Atari games and is competitive to the state-of-the-art count-based exploration methods. We also show that SIL improves proximal policy optimization (PPO) on MuJoCo tasks.

• The paper introduces a self-imitation objective that exploits rewarding past experiences to improve learning efficiency in sparse-reward environments.
• It demonstrates significant performance gains on complex tasks, outperforming baselines like A2C and enhancing methods such as PPO across diverse benchmarks.
• The study provides a theoretical foundation by connecting SIL to lower-bound soft Q-learning, indicating its broad applicability in advanced RL systems.

An Overview of "Self-Imitation Learning"

The paper "Self-Imitation Learning" introduces a reinforcement learning (RL) algorithm designed to balance between exploration and exploitation by leveraging past successful decisions made by an agent. The primary contribution of this work lies in the development of the Self-Imitation Learning (SIL) algorithm, which aims to reproduce effective experiences from an agent's history, thus facilitating deeper exploration indirectly.

The motivation behind SIL stems from the observation that exploiting past beneficial experiences can enhance learning efficiency, especially in environments where reward signals are sparse or hard to come by. SIL is essentially an off-policy actor-critic method that capitalizes on previous experiences by identifying and imitating decisions that resulted in high returns. These experiences are stored in a replay buffer, and the algorithm updates its policies by focusing only on those state-action pairs where the past return exceeds the current value estimate.

Key Components and Findings

The authors provide several compelling elements and results:

1. SIL Algorithm: The SIL method integrates a self-imitation objective into the actor-critic framework. It adjusts the policy by replaying successful past actions and applies selective experience replay prioritized by the advantage.
2. Empirical Performance: SIL demonstrated substantial improvements over baseline methods such as advantage actor-critic (A2C) across multiple challenging Atari games, including Montezuma's Revenge, which is known for its exploration difficulty. The algorithm also showed improvements when combined with proximal policy optimization (PPO) on several MuJoCo control tasks, indicating its versatility and potential applicability across different domains.
3. Theoretical Backup: The authors present a theoretical foundation for the proposed SIL objective by relating it to a lower-bound approximation of the optimal Q-function. This groundwork situates SIL within what they describe as lower-bound-soft-Q-learning, connecting it to recent understandings of the relationship between policy gradient methods and Q-learning.
4. Complement to Exploration Techniques: The paper illustrates that SIL and traditional count-based exploration strategies can be complementary. In environments with sparse rewards, SIL can enhance exploration by ensuring that the agent quickly learns to utilize any serendipitous success encounters.
5. Extensive Evaluation: The paper evaluates SIL on a comprehensive suite of 49 Atari games and shows that it outperforms or matches state-of-the-art methods in a majority of these tasks. Additionally, through a series of tests, the authors highlight that self-imitation can be particularly beneficial in delay-reward scenarios, supporting its effectiveness in diverse RL contexts.

Implications and Future Directions

The implementation of SIL could have far-reaching implications for the development of robust RL systems, particularly in domains characterized by delayed rewards or sparse feedback signals. By judiciously exploiting past successes, SIL offers a promising avenue to enhance exploration efficiency without the need for complex exploration-specific heuristics.

Future research could explore adaptive mechanisms within SIL to dynamically balance self-imitation with policy exploration, perhaps by integrating approaches that assess learning progress or adjust hyperparameters in a data-driven manner. Additionally, extending SIL to multi-agent systems and other complex environments could yield further insights and advancements in achieving scalable and efficient RL.

In conclusion, the paper "Self-Imitation Learning" articulates a compelling approach to improve RL through the effective exploitation of an agent's own history, paving the way for more intelligent and adaptable learning systems. The insights gained from this paper underscore the potential benefits of self-directed learning and highlight new paths for exploration in artificial intelligence.