Playing SNES in the Retro Learning Environment (1611.02205v2)

Abstract: Mastering a video game requires skill, tactics and strategy. While these attributes may be acquired naturally by human players, teaching them to a computer program is a far more challenging task. In recent years, extensive research was carried out in the field of reinforcement learning and numerous algorithms were introduced, aiming to learn how to perform human tasks such as playing video games. As a result, the Arcade Learning Environment (ALE) (Bellemare et al., 2013) has become a commonly used benchmark environment allowing algorithms to train on various Atari 2600 games. In many games the state-of-the-art algorithms outperform humans. In this paper we introduce a new learning environment, the Retro Learning Environment --- RLE, that can run games on the Super Nintendo Entertainment System (SNES), Sega Genesis and several other gaming consoles. The environment is expandable, allowing for more video games and consoles to be easily added to the environment, while maintaining the same interface as ALE. Moreover, RLE is compatible with Python and Torch. SNES games pose a significant challenge to current algorithms due to their higher level of complexity and versatility.

• The paper introduces the Retro Learning Environment as a versatile benchmark that expands RL evaluation to SNES and other advanced gaming consoles.
• The evaluation demonstrates that standard RL algorithms face significant challenges in complex SNES games, emphasizing the need for refined exploration and strategy methods.
• The incorporation of multi-agent setups in RLE reveals opportunities to study MARL dynamics and develop more robust, generalizable policies.

An Evaluation of the Retro Learning Environment for Reinforcement Learning

The paper "Playing SNES in the Retro Learning Environment" presents a novel reinforcement learning (RL) platform known as the Retro Learning Environment (RLE), designed to expand the variety and complexity of games available for algorithm testing. Developed by Nadav Bhonker, Shai Rozenberg, and Itay Hubara, this environment advances the state of RL evaluation by introducing games from the Super Nintendo Entertainment System (SNES) and other retro gaming consoles. RLE is structured to emulate the interface of the Arcade Learning Environment (ALE), enhancing it with broader console compatibility and multi-agent reinforcement learning (MARL) capabilities, thereby enriching the spectrum of challenges that can be addressed by current and future RL algorithms.

RLE and its Contributions

RLE provides an extension to the traditional ALE by supporting advanced gaming consoles with increased graphical and computational complexity. As delineated in the paper, SNES and Sega Genesis games, among others, introduce new difficulties for RL agents, such as richer visual detail, more complex reward mechanisms, and expanded action spaces. RLE seeks to address limitations in its predecessors by offering a unified RL interface applicable to a multitude of retro games, while maintaining compatibility with popular programming environments like Python and Torch.

Key contributions outlined in the paper include:

• Establishing RLE as a versatile RL benchmarking platform that includes multi-agent experiments, allowing RL researchers to explore MARL dynamics where agents compete or collaborate within the same gaming scenario.
• Introducing strategies to train agents against varied opponents, enhancing the robustness and generalization capabilities of learned policies.
• Facilitating the investigation of reward shaping techniques in environments with delayed rewards or multiple competing objectives, offering insights into how agents can efficiently navigate such complexities.

Evaluation and Results

The authors conducted an empirical evaluation of RLE using popular deep reinforcement learning algorithms such as DQN, Double DQN, and Dueling Double DQN. The environment's testing framework was adapted from prior methodologies where trained agents and humans engage in SNES games under standardized conditions. Their experimental results illustrated the heightened difficulty presented by SNES games relative to Atari 2600 benchmarks used in ALE. For instance, in complex games like "Wolfenstein" and "Gradius III," agents struggled to match human capabilities, underlining the demand for more sophisticated exploration and strategy learning in high-dimensional spaces with intricate reward structures.

Another significant aspect of the evaluation was the exploration of multi-agent environments and their impact on the learning process. The RLE's MARL capabilities were demonstrated using games like "Mortal Kombat," where dual-agent configurations revealed challenges in attaining a generalized policy. However, alternating opponent strategies led to improved agent robustness, potentially mitigating issues such as policy overfitting and catastrophic forgetting.

Implications and Future Directions

The RLE serves as a pivotal advancement in the field of RL, illustrating the potential for environment diversity to drive the development of more robust and adaptable RL algorithms. By embracing the complexity of SNES and similar consoles, the RLE provides an opportunity for AI research to extend its focus beyond traditional 2D games to more nuanced, real-world-resembling scenarios that demand advanced perception, planning, and decision-making capabilities.

For future directions, RLE opens avenues for the exploration of deeper neural architectures, enhanced exploration strategies, and adaptable reward mechanisms capable of handling the intricate dynamics of SNES games and similar environments. The platform's capacity for MARL research also presents opportunities to investigate cooperative and competitive agent behaviors, which are crucial in developing AI suitable for complex, interactive real-world applications.

In conclusion, the introduction of RLE marks a significant step forward in RL benchmark environments, contributing richly to both the theoretical underpinnings and practical advancements in reinforcement learning research.