Update README

README.md

@@ -1,3 +1,88 @@
----
-license: bsd-3-clause
----
+---
+license: bsd-3-clause
+tags:
+- Hopper-v2
+- reinforcement-learning
+- decisions
+- TLA
+- deep-reinforcement-learning
+model-index:
+- name: TLA
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 3449.63
+      name: mean_reward
+    - type: Action Repetition
+      value: .5557
+      name: Action Repetition
+    - type: Average Decisions
+      value: 438.87
+      name: Average Decisions
+    task:
+      type: OpenAI Gym
+      name: OpenAI Gym
+    dataset:
+      name: Hopper-v2
+      type: Hopper-v2
+  Paper: https://arxiv.org/abs/2305.18701
+  Code: https://github.com/dee0512/Temporally-Layered-Architecture
+---
+# Temporally Layered Architecture: Hopper-v2
+
+These are 10 trained models over **seeds (0-9)** of **[Temporally Layered Architecture (TLA)](https://github.com/dee0512/Temporally-Layered-Architecture)** agent playing **Hopper-v2**.
+
+## Model Sources
+
+**Repository:** [https://github.com/dee0512/Temporally-Layered-Architecture](https://github.com/dee0512/Temporally-Layered-Architecture)  
+**Paper:** [https://doi.org/10.1162/neco_a_01718](https://doi.org/10.1162/neco_a_01718)  
+**Arxiv:** [arxiv.org/abs/2305.18701](https://arxiv.org/abs/2305.18701)
+
+# Training Details:
+Using the repository:
+
+```
+python main.py --env_name <environment> --seed <seed>
+```
+
+# Evaluation:
+
+Download the models folder and place it in the same directory as the cloned repository. 
+Using the repository:
+
+```
+python eval.py --env_name <environment>
+```
+
+## Metrics:
+
+**mean_reward:** Mean reward over 10 seeds  
+**action_repeititon:** percentage of actions that are equal to the previous action  
+**mean_decisions:** Number of decisions required (neural network/model forward pass)  
+
+
+# Citation
+
+The paper can be cited with the following bibtex entry:
+
+## BibTeX:
+
+```
+@article{10.1162/neco_a_01718,
+    author = {Patel, Devdhar and Sejnowski, Terrence and Siegelmann, Hava},
+    title = "{Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures}",
+    journal = {Neural Computation},
+    pages = {1-30},
+    year = {2024},
+    month = {10},
+    issn = {0899-7667},
+    doi = {10.1162/neco_a_01718},
+    url = {https://doi.org/10.1162/neco\_a\_01718},
+    eprint = {https://direct.mit.edu/neco/article-pdf/doi/10.1162/neco\_a\_01718/2474695/neco\_a\_01718.pdf},
+}
+```
+
+## APA:
+```
+Patel, D., Sejnowski, T., & Siegelmann, H. (2024). Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures. Neural Computation, 1-30.
+```