Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a new method to study the internal memory used by reinforcement learning policies.  estimate the amount of relevant past information by estimating mutual information beten behavior histories and the current action of an agent.  perform this estimation in the passive setting, that is,  do not intervene but merely observe the natural behavior of the agent. Moreover,  provide a theoretical justification for our approach by showing that it yields an implementation-independent lor bound on the minimal memory capacity of any agent that implement the observed policy.  demonstrate our approach by estimating the use of memory of DQN policies on concatenated Atari frames, demonstrating sharply different use of memory across 49 games. The study of memory as information that flows from the past to the current action opens avenues to understand and improve successful reinforcement learning algorithms.
['Christoph Dann', 'Katja Hofmann', 'Sebastian Nowozin']
I am writing a research paper on a idea Memory Lens: How Much Memory Does an Agent Use?
[{"from": "human", "value": "I am writing a research paper on a idea Memory Lens: How Much Memory Does an Agent Use?"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a new method to study the internal memory used by reinforcement learning policies.  estimate the amount of relevant past information by estimating mutual information beten behavior histories and the current action of an agent.  perform this estimation in the passive setting, that is,  do not intervene but merely observe the natural behavior of the agent. Moreover,  provide a theoretical justification for our approach by showing that it yields an implementation-independent lor bound on the minimal memory capacity of any agent that implement the observed policy.  demonstrate our approach by estimating the use of memory of DQN policies on concatenated Atari frames, demonstrating sharply different use of memory across 49 games. The study of memory as information that flows from the past to the current action opens avenues to understand and improve successful reinforcement learning algorithms."}]