Learning to Simulate Dynamic Environments with GameGAN (2005.12126v1)

Abstract: Simulation is a crucial component of any robotic system. In order to simulate correctly, we need to write complex rules of the environment: how dynamic agents behave, and how the actions of each of the agents affect the behavior of others. In this paper, we aim to learn a simulator by simply watching an agent interact with an environment. We focus on graphics games as a proxy of the real environment. We introduce GameGAN, a generative model that learns to visually imitate a desired game by ingesting screenplay and keyboard actions during training. Given a key pressed by the agent, GameGAN "renders" the next screen using a carefully designed generative adversarial network. Our approach offers key advantages over existing work: we design a memory module that builds an internal map of the environment, allowing for the agent to return to previously visited locations with high visual consistency. In addition, GameGAN is able to disentangle static and dynamic components within an image making the behavior of the model more interpretable, and relevant for downstream tasks that require explicit reasoning over dynamic elements. This enables many interesting applications such as swapping different components of the game to build new games that do not exist.

• The paper introduces GameGAN, a novel method using GANs to simulate dynamic environments by disentangling static and dynamic elements.
• The memory module enables long-term coherence, allowing agents to navigate previously visited locations with consistent visual fidelity.
• Experimental results on Pacman and VizDoom benchmarks demonstrate superior temporal consistency and competitive reinforcement learning performance.

Learning to Simulate Dynamic Environments with GameGAN

The development of sophisticated simulators is a pivotal component in the deployment and testing of artificial agents within dynamic environments. This paper introduces GameGAN, a novel generative model that aims to simplify the creation of simulators by learning from visual and action-based input data rather than relying on explicit procedural generation or manual scripting of game logic. This approach leverages the power of generative adversarial networks (GANs) to simulate graphical games by predicting future game frames conditioned on user inputs, thereby sidestepping the labor-intensive task of hardcoding game engines.

Key Contributions

GameGAN introduces several innovations that set it apart from previous models, such as World Models and action-conditioned LSTMs. Key advancements include:

• Memory Module: GameGAN employs a novel memory module that facilitates the long-term visual consistency of the generated environments. This allows agents to return to previously visited locations, ensuring environmental coherence over time.
• Disentanglement of Static and Dynamic Components: By disentangling static background elements from dynamic foreground elements, GameGAN enhances interpretability and usability for tasks that require reasoning over dynamic entities. This disentanglement also enables flexible modifications, such as swapping in different components to create new, synthetic games.
• Adversarial Training Strategy: The training process incorporates adversarial losses via a set of discriminators—single frame, action-conditioned, and temporal discriminators—to maintain temporal coherence and realistic simulation of game environments.

Experimental Results

The paper presents the evaluation of GameGAN on two primary environments: a modified version of Pacman and the VizDoom platform. Through qualitative rollouts, GameGAN demonstrated superior ability in maintaining temporal consistency and delivering visually sharp simulations compared to baseline models. The ability to faithfully simulate Pacman's rules and dynamic behaviors, along with successful disentanglement of game components, highlights GameGAN's strengths.

Quantitative evaluations were conducted using a reinforcement learning context to measure GameGAN's simulation fidelity. The results indicated that agents trained within GameGAN environments achieved competitive performance when transferred to real game settings, validating the efficacy of the simulations produced by GameGAN.

Additionally, a "come-back-home" task was designed to measure long-term consistency. GameGAN outperformed its counterparts, reflecting the effectiveness of its memory module in maintaining environmental stability across extended sequences.

Implications and Future Work

The implications of GameGAN are substantial for both theoretical and applied domains. Theoretically, it proposes an efficient method of learning complex environment dynamics without manual intervention, providing a scalable approach to simulator creation. Practically, GameGAN offers a robust platform for developing environments where agents can learn and be evaluated, potentially simplifying processes in domains like autonomous driving simulations or robotic training.

Future directions may involve extending GameGAN to 3D environments or enhancing its capabilities by reducing reliance on labeled actions, potentially integrating unsupervised learning techniques to further abstract and generalize game mechanics. Given the focus on graphical games as proxies for real environments, future work may also address the challenges of directly applying these methodologies to more intricate, real-world scenarios.

In conclusion, GameGAN presents a significant advancement in the autonomous creation of game-based simulators, leveraging GANs to provide a flexible, high-fidelity simulation framework with applications spanning varied fields in artificial intelligence research and development.