Gated Memory Policy Networks for Non-Markovian RL Control
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Solution Overview
Problem
Current reinforcement learning methods struggle with partially observable Markov decision processes (POMDPs) and non-Markovian environments, where incomplete and noisy state information requires handcrafted state estimators and fixed memory models that are not adaptive.
Innovation Solution
A Gated End-to-End Memory Policy Network (GMemN2NP) with an attention mechanism and unbounded memory is introduced, allowing policy gradient algorithms to learn policies by leveraging past observations and updating memory dynamically, enabling effective learning in partially observed domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional reinforcement learning methods are used with fixed memory models, then the system structure is simple, but the system cannot adapt to non-Markovian environments and partially observable states
Solution Approach 1:
The patent applies dynamics by transforming the fixed memory model into a dynamic, adaptive structure. The memory model evolves from static capacity to dynamic expansion through gate mechanisms that adaptively determine when and how to store new observations. This allows the system to adjust its memory behavior based on environmental feedback and task requirements, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent changes the parameter of memory capacity from fixed to variable through the introduction of gate mechanisms. These gates dynamically control the flow of information into and out of memory, effectively changing the operational parameters of the memory system. This parameter transformation enables the system to handle non-Markovian environments while maintaining manageable complexity through learned control mechanisms.
2Reliability
If handcrafted state estimators are used for partially observable states, then the system can handle incomplete information, but the system requires accurate knowledge of the system and manual design
Solution Approach 1:
The patent applies self-service by enabling the system to automatically learn and construct its own state estimators through interaction with the environment. Instead of requiring manual design based on accurate system knowledge, the agent autonomously develops memory representations and gating mechanisms that adapt to the specific task and environmental characteristics, making the system easier to implement while maintaining reliability.
Solution Approach 2:
The patent implements feedback mechanisms where the agent's actions and observed outcomes continuously inform the updating of memory states and estimator parameters. This closed-loop learning process allows the system to refine its state estimation capabilities through experience, achieving reliable handling of partial observability without requiring prior manual design or accurate system knowledge.
3Duration of action of moving object
If fixed size memory models are used, then the system is simpler to implement, but the system cannot capture long-term dependencies in non-Markovian problems
Solution Approach 1:
The patent applies dynamics by making the memory structure adaptive rather than fixed. The gate mechanisms dynamically control which observations are stored and for how long, allowing the effective memory duration to vary based on task requirements. This dynamic approach enables capture of long-term dependencies when necessary while maintaining simpler operation when short-term memory suffices, resolving the contradiction between duration and complexity.
4Device complexity
If reactive stochastic policies are used that map observations directly to action probabilities, then the system is simpler, but the system is far from optimal and cannot effectively use memory
Solution Approach 1:
The patent introduces memory and gating mechanisms as intermediary structures between observations and action selection. Rather than directly mapping observations to actions, the system first processes observations through memory storage and retrieval operations controlled by gates. This intermediary layer enables more sophisticated policy behavior and improved optimality while adding manageable complexity through structured intermediate representations.
Data Source
AI summary
A system and method for predicting a sequence of actions employ a Gated End-to-End Memory Policy Network (GMemN2NP), which includes a sequence of hop(s). Supporting memories of the hops include memory cells generated from observations made at different times. A sequence of actions is predicted, based on input agent-specific variables. For each action, the model, at each hop, outputs an updated controller state which is used as input to the next hop or, for the terminal hop, for computing the respective action. Each hop includes a transform gate mechanism which is used to control the influence of output of the supporting memories on the updated controller state. For the second and subsequent hops, respective actions are predicted, after using any intervening observations to update the supporting memories. The model is learned, on a training set of observations, to optimize the cumulative reward of a sequence of two or more actions.