Update README.md

README.md

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 pinned: true
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+## 7/23/23 - Towards A Unified Agent with Foundation Models
+https://arxiv.org/abs/2307.09668
+
+Generate synthetic data set for the state that you want, search over the action space until you find a trajectory that reaches a cosine similarity threshold denoted by the state you want, add all those frames and states of the buffer and incorporate into training
+
+You can bootstrap process with priors still search for the desired state
+
+
+### reward
+Reward any trajectory proportionally to a semantically similar state as any state in a run with a victory condition.
+Linear or some function reward curve
+
+
+### Sample curve
+Sections of states with more changes in them
+
+
+
 ## 7/21/23
  am going to naively, without evidence, state that you can represent any function in text with a large language model.