Unsupervised Predictive Memory in a Goal-Directed Agent (1803.10760v1)

Abstract: Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of important information that is not presently available. Recently, progress has been made with AI agents that learn to perform tasks from sensory input, even at a human level, by merging reinforcement learning (RL) algorithms with deep neural networks, and the excitement surrounding these results has led to the pursuit of related ideas as explanations of non-human animal learning. However, we demonstrate that contemporary RL algorithms struggle to solve simple tasks when enough information is concealed from the sensors of the agent, a property called "partial observability". An obvious requirement for handling partially observed tasks is access to extensive memory, but we show memory is not enough; it is critical that the right information be stored in the right format. We develop a model, the Memory, RL, and Inference Network (MERLIN), in which memory formation is guided by a process of predictive modeling. MERLIN facilitates the solution of tasks in 3D virtual reality environments for which partial observability is severe and memories must be maintained over long durations. Our model demonstrates a single learning agent architecture that can solve canonical behavioural tasks in psychology and neurobiology without strong simplifying assumptions about the dimensionality of sensory input or the duration of experiences.

• The paper presents MERLIN's innovative predictive modeling that forms compressed state representations from sensory inputs in partially observed tasks.
• It integrates deep LSTM networks with a structured memory system, enabling efficient storage and retrieval of high-dimensional data without immediate reward reliance.
• The system demonstrates superior performance in goal-directed navigation, rapid reward valuation, and episodic memory tasks compared to conventional RL models.

Analysis of the MERLIN Model for Unsupervised Predictive Memory in Goal-Directed Agents

The paper presents a comprehensive paper of the Memory, RL, and Inference Network (MERLIN), an architecture designed to address the challenges posed by partially observed environments in reinforcement learning (RL). The research identifies the limitations of extant RL algorithms, particularly when agents operate in conditions of partial observability, emphasizing that not only is extensive memory necessary, but the information stored must also be accurately formatted.

Overview of MERLIN Architecture

The MERLIN architecture integrates external memory systems with reinforcement learning and inference models to manage the challenges of partial observability. The key components are a Memory-Based Predictor (MBP) and a policy network, both equipped with deep Long Short-Term Memory (LSTM) networks. The MBP is enhanced with a structured memory system similar to a Differentiable Neural Computer (DNC), enabling the agent to store high-dimensional sensory input and retrieve relevant information when required. Importantly, the system is optimized based on unsupervised predictive modeling, diverging from traditional end-to-end RL approaches.

Key Insights from MERLIN's Design

1. Predictive Modeling: MERLIN uses predictive modeling as a basis for memory formation. This approach allows the architecture to generate compressed state representations from sensory observations, which are foundational in addressing partial observability.
2. Memory Storage and Retrieval: The architecture effectively stores state variables in memory, integrating information over time. Crucially, memory retrieval does not rely directly on the policy’s performance signal but instead uses a predictive mechanism, which enhances data efficiency and robustness across long-duration tasks.
3. Task Performance: MERLIN demonstrates superior performance across a range of tasks, including goal-directed navigation, rapid reward valuation, and tasks requiring episodic memory, outperforming baseline RL models like RL-LSTM and RL-MEM.
4. Hierarchical Memory Behavior: An emergent property in MERLIN is its ability to develop hierarchical behavioral patterns through its memory read operations, showcasing specialization in recalling information formed at various distances from a goal.

Implications and Future Directions

Practical Implications: By decoupling memory representation from immediate task reward, MERLIN paves the way for developing agents that can function effectively in environments with high partial observability. The implications extend to real-world applications where sensing capabilities are inherently limited.

Theoretical Implications: MERLIN challenges the prevailing methodology of end-to-end RL learning, suggesting that architecture-induced memory systems can lead to better performance in scenarios requiring long-term memory.

Future Developments: The paper opens various avenues for further research, such as exploring different architectures for predictive modeling and memory systems and investigating the scalability of MERLIN-like architectures to more complex and dynamic environments. Additionally, exploring the role of predictive models in other domains, like adaptive control systems or in settings with incomplete data, represents a promising direction.

In conclusion, MERLIN demonstrates a significant paradigm shift in addressing the limitations of RL in partially observed environments by leveraging unsupervised predictive memory models. Its architecture offers valuable insights into the importance of structured memory and inference in developing robust goal-directed agents.