Long horizon episodic decision making for cognitively inspired robots

The Human decision-making process works by recollecting past sequences of observations and using them to decide the best possible action in the present. These past sequences of observations are stored in a derived form which only includes important information the brain thinks might be useful in the future, while forgetting the rest. we propose an architecture that tries to mimic the human brain and improve the memory efficiency of transformers by using a modified TransformerXL architecture which uses Automatic Chunking which only attends to the relevant chunks in the transformer block. On top of this, we use ForgetSpan which is technique to remove memories that do not contribute to learning. We also theorize the technique of Similarity based forgetting to remove repetitive memories. We test our model in various tasks that test the abilities required to perform well in a human–robot collaboration scenario.