Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments (2301.13758v2)

Abstract: Model-based next state prediction and state value prediction are slow to converge. To address these challenges, we do the following: i) Instead of a neural network, we do model-based planning using a parallel memory retrieval system (which we term the slow mechanism); ii) Instead of learning state values, we guide the agent's actions using goal-directed exploration, by using a neural network to choose the next action given the current state and the goal state (which we term the fast mechanism). The goal-directed exploration is trained online using hippocampal replay of visited states and future imagined states every single time step, leading to fast and efficient training. Empirical studies show that our proposed method has a 92% solve rate across 100 episodes in a dynamically changing grid world, significantly outperforming state-of-the-art actor critic mechanisms such as PPO (54%), TRPO (50%) and A2C (24%). Ablation studies demonstrate that both mechanisms are crucial. We posit that the future of Reinforcement Learning (RL) will be to model goals and sub-goals for various tasks, and plan it out in a goal-directed memory-based approach.

• The paper introduces a dual-system approach that combines fast, goal-directed exploration with slower memory-based retrieval to enable rapid adaptation in dynamic settings.
• The proposed method outperforms traditional actor-critic algorithms by achieving a 92% solve rate in 10x10 grid experiments, demonstrating superior efficiency.
• This framework shifts focus from strict optimization to satisficing strategies, offering practical insights for applications like autonomous systems in unpredictable environments.

An Analytical Review of a Goal-Directed Memory-Based Approach for Reinforcement Learning in Dynamic Environments

The paper, "Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments," presents a novel reinforcement learning (RL) framework designed to enhance adaptability and efficiency in dynamic settings. The authors propose a dual mechanism system that combines a goal-directed neural network (fast) with a memory-based retrieval system (slow). This approach steps away from the traditional optimization-focused RL paradigms, suggesting instead a satisficing strategy that favors rapid adaptation over seeking optimality in rapidly changing environments.

Key Components of the Approach

1. Goal-Directed Mechanism (Fast): This component employs a goal-conditioned neural network that predicts the next action based on the current and goal states. It leverages goal-directed exploration, enhancing the agent's ability to make efficient exploratory moves in the environment. The fast mechanism is integral to quick decision-making and initial direction-setting, acting as an efficient compass for navigation tasks.
2. Memory-Based Mechanism (Slow): Parallel memory retrieval allows the system to use past experiences effectively. Instead of employing neural networks for next-state predictions—often slow and inefficient—the system utilizes an external memory that stores environmental transition histories. This memory-based planning affords the agent an exploration strategy grounded in previous experiences, improving adaptability and reducing reliance on slow-train next-state predictions.

The combination of these mechanisms is demonstrated to outperform traditional actor-critic algorithms such as PPO, TRPO, and A2C. Experimental results reveal a high solve rate of 92% across varied episodes in a dynamically changing grid task, markedly surpassing the performance of the state-of-the-art methods.

Experimental Insights

The empirical evaluations underscore the proposed method's adaptability and efficiency. In static and dynamic 10x10 grid environments, the system showed superior solve rates and reduced time steps compared to traditional methods. The use of external memory for world modeling mitigates the inefficiencies of state-transition predictions, illustrated by extensive training in RL benchmarks like MuZero. The proposed memory-based approach manages to provide reliable real-time adaptiveness without extensive computational overhead.

Theoretical and Practical Implications

From a theoretical standpoint, the research promotes a shift towards goal-oriented RL methods, where optimization is secondary to rapid adaptability and scalability. The introduction of a dual mechanism approach dovetails with existing cognitive models, suggesting a biologically plausible strategy for artificial systems. Memory retrieval parallels cognitive processes observed in nature, such as the use of temporal sequences and episodic memories for decision-making.

Practically, this framework offers significant potential for real-world applications where environments are unpredictable and dynamic. The implications extend to autonomous systems, like self-driving cars, where situational changes are frequent, and decision-making should be agile and minimally dependent on exhaustive exploration for optimality.

Future Directions

The paper concludes with potential extensions, such as multi-agent learning and scaling to continuous domains. Incorporating advanced natural language processing for goal abstraction could broaden the method’s applicability to less structured domains. Additionally, investigations into adaptive memory mechanisms, akin to cognitive forgetting processes, may further enhance system adaptability.

Conclusion

The paper sets forth a compelling framework for fast, memory-enhanced RL, backed by empirical evidence of superior performance in both static and dynamic settings. By prioritizing rapid adaptability through strategic memory use and goal-directed exploration, the research paves the way for new directions in RL, offering a robust alternative to conventional optimization-centric methods.