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HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning

Quentin Delfosse, Jannis Blüml, Bjarne Gregori, Kristian Kersting

TL;DR

HackAtari addresses generalization and alignment gaps in reinforcement learning by injecting controlled novelty into the Atari Learning Environments. It defines a modular framework that alters visuals, dynamics, curricula, and rewards across 16 Atari games (50 variants) using RAM-level mappings and OCAtari representations. Empirical results with PPO and C51 show both robust training on variants and clear misgeneralization when agents face unseen changes, while also enabling curriculum learning and LLM-guided reward design. The framework supports neuro-symbolic RL and continual RL, offering a practical path toward more robust, interpretable, and adaptable RL systems for real-world deployment.

Abstract

Artificial agents' adaptability to novelty and alignment with intended behavior is crucial for their effective deployment. Reinforcement learning (RL) leverages novelty as a means of exploration, yet agents often struggle to handle novel situations, hindering generalization. To address these issues, we propose HackAtari, a framework introducing controlled novelty to the most common RL benchmark, the Atari Learning Environment. HackAtari allows us to create novel game scenarios (including simplification for curriculum learning), to swap the game elements' colors, as well as to introduce different reward signals for the agent. We demonstrate that current agents trained on the original environments include robustness failures, and evaluate HackAtari's efficacy in enhancing RL agents' robustness and aligning behavior through experiments using C51 and PPO. Overall, HackAtari can be used to improve the robustness of current and future RL algorithms, allowing Neuro-Symbolic RL, curriculum RL, causal RL, as well as LLM-driven RL. Our work underscores the significance of developing interpretable in RL agents.

HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning

TL;DR

HackAtari addresses generalization and alignment gaps in reinforcement learning by injecting controlled novelty into the Atari Learning Environments. It defines a modular framework that alters visuals, dynamics, curricula, and rewards across 16 Atari games (50 variants) using RAM-level mappings and OCAtari representations. Empirical results with PPO and C51 show both robust training on variants and clear misgeneralization when agents face unseen changes, while also enabling curriculum learning and LLM-guided reward design. The framework supports neuro-symbolic RL and continual RL, offering a practical path toward more robust, interpretable, and adaptable RL systems for real-world deployment.

Abstract

Artificial agents' adaptability to novelty and alignment with intended behavior is crucial for their effective deployment. Reinforcement learning (RL) leverages novelty as a means of exploration, yet agents often struggle to handle novel situations, hindering generalization. To address these issues, we propose HackAtari, a framework introducing controlled novelty to the most common RL benchmark, the Atari Learning Environment. HackAtari allows us to create novel game scenarios (including simplification for curriculum learning), to swap the game elements' colors, as well as to introduce different reward signals for the agent. We demonstrate that current agents trained on the original environments include robustness failures, and evaluate HackAtari's efficacy in enhancing RL agents' robustness and aligning behavior through experiments using C51 and PPO. Overall, HackAtari can be used to improve the robustness of current and future RL algorithms, allowing Neuro-Symbolic RL, curriculum RL, causal RL, as well as LLM-driven RL. Our work underscores the significance of developing interpretable in RL agents.
Paper Structure (28 sections, 1 equation, 9 figures, 5 tables)

This paper contains 28 sections, 1 equation, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. HackAtari: Altered Atari Environments
  3. HackAtari: step, reset and reward modifications
  4. Testing visual and dynamics robustness, curriculum RL and adaptability.
  5. Experimental Evaluation
  6. HackAtari modified environments can be used for learning (Q1).
  7. HackAtari can help uncover flaws of trained agents (Q2).
  8. HackAtari allows to learn alternative behaviors (Q3).
  9. Simplifications enable skill learning (Q4).
  10. Related Work
  11. Novelty in RL.
  12. Evaluating Generalization and Robustness.
  13. Continual reinforcement learning benchmarks.
  14. Limitations
  15. Conclusion
  16. ...and 13 more sections

Figures (9)

  • Figure 1: Examples of misaligned agents. In Coinrun (left), agents learn to reach the end of the level, instead of the coin. In Pong (right), agents learn to follow the enemy instead of the ball. Importance maps (top) are not enough for detecting such misalignments, environment variations are necessary.
  • Figure 2: RAM alteration allows for modified environments. Exemplified on Freeway. Altering the some RAM cells leads to color and speed changes.
  • Figure 3: HackAtari provides variations of Atari environments. These include color changes (Freeway and Boxing), gameplay shifts (Boxing, MsPacman), continual learning settings (Kangaroo and Frostbite). The original games (top) are compared to HackAtari's modified versions (bottom). Superposed frames show the game dynamics.
  • Figure 4: RL agents can learn on altered environments, exemplified on One armed Boxing, Mono-Colored Freeway and Lazy Enemy Pong, by PPO and C51 agents. These agents are able to progressively improve from random to (or beyond) the human level. Freeway's high variance is due to the number of frames needed before each seeded agent reaches the top.
  • Figure 5: LLM can guide RL agents. Performances of PPO agents trained using an LLM-provided reward function (blue) and the original reward (orange).
  • ...and 4 more figures