Update Readme

README.md

@@ -4,12 +4,13 @@ tags:
 - Pendulum-v1
 - Reinforcement-Learning
 - Decisions
+- TLA
 model-index:
 - name: TLA
   results:
   - metrics:
     - type: mean_reward
-      value: -154.92 +/- 31.97
+      value: '-154.92 +/- 31.97'
       name: mean_reward
     - type: action_repetition
       value: 70.32%
@@ -24,4 +25,59 @@ model-index:
       name: Pendulum-v1
       type: Pendulum-v1
 ---
-# Temporally Layered Architecture: Pendulum-v1
+# Temporally Layered Architecture: Pendulum-v1
+
+These are 10 trained models over **seeds (0-9)** of **[Temporally Layered Architecture (TLA)](https://github.com/dee0512/Temporally-Layered-Architecture)** agent playing **Pendulum-v1**.
+
+## 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]
+
+# Training Details:
+Using the repository:
+
+```
+python main.py --env_name <environment> --seed <seed>
+```
+
+# Evaluation:
+
+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.
+```