Add paper link, license and usage example

README.md

@@ -1,13 +1,52 @@
 ---
-pipeline_tag: reinforcement-learning
 library_name: transformers
+pipeline_tag: reinforcement-learning
 tags:
 - In-Context RL
+license: mit
 ---
+
 # Mixture-of-Experts Meets In-Context Reinforcement Learning
+
 ## Sources
 - **Repository:** [Github](https://github.com/NJU-RL/T2MIR)
-- **Paper:** [Mixture-of-Experts Meets In-Context Reinforcement Learning](https://arxiv.org/abs/2506.05426)
+- **Paper:** [Mixture-of-Experts Meets In-Context Reinforcement Learning](https://huggingface.co/papers/2506.05426)
 
 ## Model Description
-Checkpoints of T2MIR-AD and T2MIR-DPT on Cheetah-Vel using mixed datasets.
+Checkpoints of T2MIR-AD and T2MIR-DPT on Cheetah-Vel using mixed datasets.
+
+## Usage
+
+The provided code implements the T2MIR framework. See the [GitHub repository](https://github.com/NJU-RL/T2MIR) for training and evaluation instructions. Examples are provided below for training and evaluating `T2MIR-AD` and `T2MIR-DPT` on the Cheetah-Vel environment.
+
+**Training**
+
+To train `T2MIR-AD`, use:
+
+```bash
+cd T2MIR-AD
+python train.py cheetah-vel-v0 --exp exp_0 --seed 3407
+```
+
+To train `T2MIR-DPT`, use:
+
+```bash
+cd T2MIR-DPT
+python train.py cheetah-vel-v0 --exp exp_0 --seed 3407
+```
+
+**Evaluation**
+
+To evaluate `T2MIR-AD`, use:
+
+```bash
+cd T2MIR-AD
+python eval.py cheetah-vel-v0 1 --exp exp_0 --seed 3407 --start-ckpt 89000 --stop-ckpt 89000 --seed-eval 10
+```
+
+To evaluate `T2MIR-DPT`, use:
+
+```bash
+cd T2MIR-DPT
+python eval_online.py cheetah-vel-v0 1 --exp exp_0 --seed 3407 --start-ckpt 59000 --stop-ckpt 59000 --seed-eval 10
+```