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CALE: Continuous Arcade Learning Environment (2410.23810v1)

Published 31 Oct 2024 in cs.LG and cs.AI

Abstract: We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare et al., 2013]. The CALE uses the same underlying emulator of the Atari 2600 gaming system (Stella), but adds support for continuous actions. This enables the benchmarking and evaluation of continuous-control agents (such as PPO [Schulman et al., 2017] and SAC [Haarnoja et al., 2018]) and value-based agents (such as DQN [Mnih et al., 2015] and Rainbow [Hessel et al., 2018]) on the same environment suite. We provide a series of open questions and research directions that CALE enables, as well as initial baseline results using Soft Actor-Critic. CALE is available as part of the ALE athttps://github.com/Farama-Foundation/Arcade-Learning-Environment.

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Summary

  • The paper introduces CALE, a novel benchmark that extends ALE by incorporating continuous action spaces into Atari 2600 games.
  • It evaluates the performance of continuous control algorithms like SAC against discrete methods such as DQN, highlighting key performance disparities.
  • Initial results reveal that SAC underperforms compared to discrete-action algorithms, indicating the need for improved tuning and adaptation in continuous RL.

Continuous Arcade Learning Environment: A Comprehensive Overview

The paper introduces the Continuous Arcade Learning Environment (CALE), an innovative extension to the well-regarded Arcade Learning Environment (ALE), significantly enhancing the versatility and applicability of reinforcement learning (RL) benchmarks. By incorporating continuous action spaces into the existing ALE framework, CALE presents a unified platform that allows for the evaluation of both discrete and continuous-action agents on Atari 2600 games. This paper makes a substantial contribution to the artificial intelligence and machine learning community by providing a robust baseline for testing the generality, capability, and autonomy of learning agents under more realistic interaction paradigms that mimic human control.

Core Contributions

At the heart of this paper lies the implementation of CALE, which maintains the Atari 2600's foundational game mechanics while transitioning from a fixed set of discrete actions to a continuous action space. This transition is crucial for enabling the evaluation of agents employing continuous control algorithms such as Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO), as well as traditional value-based algorithms like Deep Q-Networks (DQN) and Rainbow.

A detailed analysis is provided, encompassing the potential research directions enabled by CALE, including exploration, network architectures, offline RL, and action parameterization. The paper presents initial baseline results using SAC and highlights the disparities in agent performance, pinpointing areas that warrant further investigation. Notably, the authors report that SAC underperforms relative to discrete-action algorithms like DQN in the evaluated benchmark settings, pointing to a need for better tuning and adaptation of continuous control methods to the unique challenges posed by CALE.

Numerical Results and Claims

The reported baseline experiments reveal that SAC, when evaluated on the CALE, achieves an Interquartile Mean (IQM) significantly below the normalized human-level performance benchmark. This indicates that there is substantial room for improvement in continuous-action RL methodologies. The comparative performance analysis across several Atari 2600 games underscores both the potential and the limitations of continuous-action agents, showing varied results with some games outperforming discrete-action baselines, while others fall considerably short.

Implications and Future Directions

The introduction of CALE has profound implications for both the theoretical and practical development of AI. The ability to evaluate diverse types of RL agents using a single benchmark environment facilitates a more comprehensive understanding of their respective strengths and weaknesses. This could lead to more robust AI systems that incorporate the best elements of both discrete and continuous control methodologies.

The paper also identifies several avenues for future research. These include refining exploration strategies for continuous-action agents, optimizing network architectures for improved performance, and leveraging the characteristics of CALE to explore offline RL in new ways. Specifically, the paper points out the potential for CALE to contribute to advancements in exploration techniques that might outperform traditional epsilon-greedy approaches, as well as the value of experimenting with different action parameterizations.

Conclusion

In conclusion, CALE represents a significant step forward in the evolution of RL benchmarks, offering the research community an enriched platform for the development and evaluation of more capable and autonomous agents. While the initial findings emphasize the challenges ahead, including the need for effective tuning and algorithm adaptation, the potential for groundbreaking insights and advancements in RL and AI is substantial. As researchers continue to explore and expand upon this work, CALE is poised to become a cornerstone in future developments, facilitating the creation of more intelligent and adaptable systems that can navigate complex environments with both discrete and continuous action requirements.

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