update README.md

  * pommerman training code .yml.jinja2
  * pommerman model

README.md

@@ -70,7 +70,7 @@ see the [link here](https://github.com/tencent-ailab/Arena#dependencies).
 [Here are examples](https://github.com/tencent-ailab/TLeague/blob/dev-open/docs/EXAMPLE_SM.md#vizdoom)
 of how to train ViZDoom in a single machine.
 
-Refer also to the [link here](https://github.com/tencent-ailab/TLeagueVdAutoBuild.git) for how to (auto-)build the docker image,
+Refer also to the [link here](https://github.com/tencent-ailab/TLeagueAutoBuild/tree/dev-open) for how to (auto-)build the docker image,
 which is yet-another guide to installation from scratch.
 
 For running training over a k8s cluster, see the [link here](vizdoom/README.md#training-code).
@@ -83,9 +83,10 @@ see the [link here](https://github.com/tencent-ailab/Arena#dependencies).
 [Here are examples](https://github.com/tencent-ailab/TLeague/blob/dev-open/docs/EXAMPLE_SM.md#pommerman)
 for how to train Pommerman in a single machine. 
 
-TODO: pointer to the Docker Auto Build repo and say it's yet-another guide to installation from scratch.
+Refer also to the [link here](https://github.com/tencent-ailab/TLeagueAutoBuild/tree/pommerman) for how to (auto-)build the docker image,
+which is yet-another guide to installation from scratch.
 
-TODO: texts for how to train with k8s
+For running training over a k8s cluster, see the [link here](pommerman/README.md#training-code).
 
 ### Single Agent RL
 TLeague also works for pure RL,

pommerman/README.md

@@ -1,9 +1,38 @@
 # Pommerman Experiments
 This page contains the resources for the experiments of Pommerman as discussed in the TLeague technical report.
 
-TODO: link to the trained models
 
+## Training Code
+The training yaml in the technical reports can be generated by [pommerman.yml.jinja2](pommerman.yml.jinja2).
+
+## Evaluation
+```
+python3 -m tleague.sandbox.run_local_battle_pommerman \
+--policy_config="{
+    'use_xla': False,
+    'rollout_len': 1,
+    'test': True,
+    'rl': False,
+    'use_loss_type': 'none',
+    'use_value_head': False,
+    'use_self_fed_heads': True,
+    'use_lstm': True,
+    'nlstm': 64,
+    'hs_len': 128,
+    'lstm_duration': 1,
+    'lstm_dropout_rate': 0.0,
+    'lstm_cell_type': 'lstm',
+    'lstm_layer_norm': True,
+    'weight_decay': 0.00000002,
+    'n_v': 11,
+    'merge_pi': False,
+  }" \
+--model=0076:0077_20201029114642.model
+```
 ## Downloads
+### Trained Model
+The trained model (after 10 day's training) can be downloaded at [Google Drive](https://drive.google.com/file/d/125eUbQl0QTw9f4uyGTcTxMR6GUfRvPBE/view?usp=sharing) 
+or [Tencent Weiyun](https://share.weiyun.com/hkLvLNT0).
 ### Replay Files
 Here are the 100 replay files of our agent against Navocado as discussed in the TLeague technical report:
 [Google Drive](https://drive.google.com/file/d/1miuqo7EpzgNIGHUNtPqdIswe8rKuoRk0/view?usp=sharing)