Add docs for running

README.md

@@ -8,29 +8,21 @@ YGO Agent is a project to create a Yu-Gi-Oh! AI using deep learning (LLMs, RL).
 ## ygoai
 `ygoai` is a set of AI agents for playing Yu-Gi-Oh! It aims to achieve superhuman performance like AlphaGo and AlphaZero, with or without human knowledge. Currently, we focus on using reinforcement learning to train the agents.
 
-## TODO
-
-### Documentation
-
-- Add documentations of building and running
 
-### Training
-- Eval with old models during training
-- MTCS-based training
+## Usage
 
-### Inference
-- MCTS-based planning
-- Support of play in YGOPro
+### Obtain a trained agent
 
+We provide some trained agents in the [releases](https://github.com/sbl1996/ygo-agent/releases/tag/v0.1). Check these `ptj` TorchScript files and download them to your local machine. The following usage assumes you have it.
 
-## Usage
+Notice that the provided `ptj` can only run on GPU, but not CPU. Actually, the agent can run in real-time on CPU, we will provide a CPU version in the future.
 
-### Serialize agent
+### Play against the agent
 
-After training, we can serialize the trained agent model to a file for later use without keeping source code of the model. The serialized model file will end with `.ptj` (PyTorch JIT) extension.
+We can use `eval.py` to play against the trained agent with a MUD-like interface in the terminal.
 
 ```bash
-python -u eval.py --agent --checkpoint checkpoints/1234_1000M.pt --num_embeddings 999 --convert --optimize
+python -u eval.py --agent --deck ../assets/deck  --lang chinese --checkpoint checkpoints/1234_1000M.ptj --play
 ```
 
 ### Battle between two agents
@@ -41,8 +33,31 @@ We can use `battle.py` to let two agents play against each other and find out wh
 python -u battle.py --deck ../assets/deck --checkpoint1 checkpoints/1234_1000M.ptj --checkpoint2 checkpoints/9876_100M.ptj --num-episodes=256 --num_envs=32 --seed 0
 ```
 
-### Running
-TODO
+You can set `--num_envs=1 --verbose --record` to generate `.yrp` replay files.
+
+
+### Serialize agent
+
+After training, we can serialize the trained agent model to a file for later use without keeping source code of the model. The serialized model file will end with `.ptj` (PyTorch JIT) extension.
+
+```bash
+python -u eval.py --agent --checkpoint checkpoints/1234_1000M.pt --num_embeddings 999 --convert --optimize
+```
+
+## TODO
+
+### Documentation
+
+- Add documentations of building and running
+
+### Training
+- Evaluation with old models during training
+- LSTM for memory
+- League training following AlphaStar and ROA-Star
+
+### Inference
+- MCTS-based planning
+- Support of play in YGOPro
 
 
 ## Related Projects

scripts/eval.py

@@ -142,6 +142,9 @@ if __name__ == "__main__":
     envs = RecordEpisodeStatistics(envs)
 
     if args.agent:
+        if args.checkpoint.endswith(".ptj"):
+            agent = torch.jit.load(args.checkpoint)
+        else:
             # count lines of code_list
             embedding_shape = args.num_embeddings
             if embedding_shape is None: