Imagination-Augmented Agents for Deep Reinforcement Learning (1707.06203v2)

Abstract: We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a learned environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several baselines.

• The paper introduces Imagination-Augmented Agents (I2As) that combine model-free and model-based approaches by simulating future states to enhance decision-making.
• The architecture employs an imagination core and rollout encoder, achieving about 85% success in solving Sokoban levels compared to conventional methods.
• The method improves data efficiency and generalization, suggesting significant applications in robotics, autonomous systems, and strategic game environments.

Imagination-Augmented Agents for Deep Reinforcement Learning

The paper introduces Imagination-Augmented Agents (I2As), a novel approach in the domain of deep reinforcement learning (RL) that integrates both model-free and model-based techniques. This architecture provides a mechanism for agents to leverage an internal environment model to enhance decision-making and performance, addressing some of the well-known limitations associated with conventional RL strategies.

Key Contributions

The I2A framework utilizes imagination to simulate potential scenarios, thus allowing agents to learn from hypothetical experiences. This method contrasts with typical model-based approaches by not dictating a strict policy derivation from models, but instead learning to interpret these simulations as additional context within policy networks. The architecture comprises several components:

1. Imagination Core: Predicts future states conditioned on chosen actions, facilitating imagined trajectories.
2. Rollout Encoder: Processes these imagined sequences to extract meaningful information, allowing agents to derive benefit from imperfect model predictions without relying solely on simulated returns.
3. Policy Network: Integrates information from both model-free and model-based paths, resulting in a policy that benefits from internal simulations.

Numerical Results and Observations

The I2A architecture exhibits enhanced data efficiency, improved performance, and robustness against model misspecification across various benchmarks when compared to traditional model-free and model-based reinforcement learning baselines. Notable experiments were conducted in complex environments such as Sokoban, revealing that I2As outperform standard RL architectures by leveraging imperfect models effectively.

1. Model Performance: I2As solve approximately 85% of Sokoban levels in contrast to less than 60% by conventional model-free agents, even with short rollout lengths which are resource-efficient.
2. Imagination Length Impact: Increasing the imagination depth in rollouts enhanced performance, indicating that richer imagined contexts yield better decision-making, although with diminishing returns beyond a certain depth.
3. Generalization: I2As demonstrated the capacity to generalize across varying conditions within the same environment type, as evidenced by their performance in levels with different complexities.

Implications and Future Directions

The introduction of I2As presents both practical and theoretical implications for the field of AI and RL. By providing a framework that successfully harnesses the advantages of model-based thinking without being hamstrung by the inaccuracies of learned models, I2As suggest a promising direction for creating more flexible and efficient learning agents.

• Theoretical Implications: The approach emphasizes the importance of learning to interpret model predictions contextually, thus, broadening the traditional perspectives on model-based planning where exactitude in state transitions is pivotal.
• Practical Applications: I2As could be particularly beneficial in fields where interaction costs or the possibility of irreversible actions are significant, such as robotics, autonomous vehicles, and strategic games.

Future Developments: Exploring dynamic resource allocation to manage compute costs associated with rollout strategies, and developing abstract environment models to streamline and scale I2As to more intricate domains, are promising areas for future research. Additionally, optimizing the construction of the rollout policy to focus imagination on the most pertinent parts of the state space could further refine the effectiveness of I2As.

In summary, the Imagination-Augmented Agents provide a significant step forward in bridging the gap between model-free and model-based RL, offering a more nuanced and versatile approach to enhancing agent learning and adaptability.