Control of Memory, Active Perception, and Action in Minecraft (1605.09128v1)

Abstract: In this paper, we introduce a new set of reinforcement learning (RL) tasks in Minecraft (a flexible 3D world). We then use these tasks to systematically compare and contrast existing deep reinforcement learning (DRL) architectures with our new memory-based DRL architectures. These tasks are designed to emphasize, in a controllable manner, issues that pose challenges for RL methods including partial observability (due to first-person visual observations), delayed rewards, high-dimensional visual observations, and the need to use active perception in a correct manner so as to perform well in the tasks. While these tasks are conceptually simple to describe, by virtue of having all of these challenges simultaneously they are difficult for current DRL architectures. Additionally, we evaluate the generalization performance of the architectures on environments not used during training. The experimental results show that our new architectures generalize to unseen environments better than existing DRL architectures.

• The paper introduces cognition-inspired RL tasks in Minecraft designed to challenge conventional DRL architectures with partial observability, delayed rewards, and complex perception demands.
• The study presents memory-based architectures like RMQN and FRMQN that leverage temporal context to improve decision-making in dynamic and high-dimensional environments.
• Empirical results demonstrate that these advanced models significantly enhance generalization and performance in unseen tasks through effective memory retrieval and active perception control.

Analysis of Memory, Perception, and Action Control in Minecraft Reinforcement Learning Tasks

The paper "Control of Memory, Active Perception, and Action in Minecraft" provides a detailed exploration of novel reinforcement learning (RL) tasks within the 3D environment of Minecraft. The paper introduces tasks that are intentionally designed to impose significant challenges on existing deep reinforcement learning (DRL) architectures, particularly concerning partial observability, delayed rewards, and the dynamic application of active perception.

Overview of Key Contributions

The authors make several significant contributions:

1. Introduction of Cognition-Inspired Tasks: They propose bespoke RL tasks set in Minecraft, a versatile 3D environment, designed to mimic cognitive processes by incorporating challenges like partial observability due to first-person perspective, delayed rewards, and the necessity for sophisticated perception strategies.
2. Memory-Based DRL Architectures: The paper contrasts existing DRL setups with advanced, bespoke memory-based architectures. These new architectures aim to tackle the tasks' inherent challenges by integrating temporal context into memory retrieval, thereby improving task execution in situations where standard architectures fall short.
3. Generalization to Unseen Environments: One of the core evaluation metrics in the paper is the ability of these architectures to generalize from trained environments to novel, unseen tasks. The authors provide empirical evidence suggesting superior performance of their proposed architectures in such generalization tasks, as opposed to existing architectures.

Experimental Methodology

The authors designed their experiments to thoroughly evaluate the capability of DRL architectures to perform well on cognition-demanding tasks. The Minecraft environment is manipulated to construct tasks characterized by high-dimensional observations and dynamic goals. For instance, in the described I-Maze task, agents must remember color cues and navigate according to changing goal locations, requiring a robust memory and perception mechanism. The presented models (DQN, DRQN, MQN, RMQN, and FRMQN) underwent extensive comparison based on performance metrics on both training and unforeseen tasks.

Numerical Results and Generalization

The architectures incorporating memory-based query systems (RMQN and FRMQN) demonstrated an enhanced capacity to generalize beyond trained environments. Notably, the RMQN and FRMQN architectures outperformed others on large-scale I-Mazes, illustrating their prowess at managing partial observability and undertaking more nuanced decision-making based on visual pattern recognition. Moreover, the FRMQN's feedback loop for context-enhanced memory retrieval enables complex reasoning, efficiently demonstrated in pattern recognition tasks.

Theoretical and Practical Implications

The results underscore the limitations of conventional DRL architectures in environments demanding cognition-like operations, highlighting the importance of memory mechanisms that consider temporal context. This work propels the relevant discourse surrounding RL in cognitive tasks by stressing memory's role in handling real-world-like conditions in simulated environments, thus pushing for an evolution of strategies to deal with episodic memory challenges.

Future Developments

Looking forward, this research could influence ongoing efforts to resolve memory-related inefficiencies in RL. Continued research might explore integrating these advanced architectures in more varied cognitive tasks, potentially reaching applications in autonomous agents requiring high understanding under variable observability conditions. The newfound ability to generalize learned behaviors across distinct tasks might further contribute to developing robust AI systems transferable across domains.

In conclusion, the paper comprehensively introduces complex RL tasks drawing inspiration from cognitive problems, presents innovative architectures tailored to these tasks, and demonstrates improved generalization, marking a valuable progression in the exploration of memory-augmented architectures within artificial intelligence.