Pytorch Implementation of MuZero : "Mastering Atari , Go, Chess and Shogi by Planning with a Learned Model" based on pseudo-code provided by the authors
Note: This implementation has just been tested on CartPole-v1 and would required modifications(in config folder) for other environments
- Python 3.6, 3.7
-
cd muzero-pytorch pip install -r requirements.txt
- Train:
python main.py --env CartPole-v1 --case classic_control --opr train --force - Test:
python main.py --env CartPole-v1 --case classic_control --opr test - Visualize results :
tensorboard --logdir=<result_dir_path>
| Required Arguments | Description |
|---|---|
--env |
Name of the environment |
--case {atari,classic_control,box2d} |
It's used for switching between different domains(default: None) |
--opr {train,test} |
select the operation to be performed |
| Optional Arguments | Description |
|---|---|
--value_loss_coeff |
Scale for value loss (default: None) |
--revisit_policy_search_rate |
Rate at which target policy is re-estimated (default:None)( only valid if --use_target_model is enabled) |
--use_priority |
Uses priority for data sampling in replay buffer. Also, priority for new data is calculated based on loss (default: False) |
--use_max_priority |
Forces max priority assignment for new incoming data in replay buffer (only valid if --use_priority is enabled) (default: False) |
--use_target_model |
Use target model for bootstrap value estimation (default: False) |
--result_dir |
Directory Path to store results (defaut: current working directory) |
--no_cuda |
no cuda usage (default: False) |
--debug |
If enables, logs additional values (default:False) |
--render |
Renders the environment (default: False) |
--force |
Overrides past results (default: False) |
--seed |
seed (default: 0) |
--test_episodes |
Evaluation episode count (default: 10) |
Note: default: None => Values are loaded from the corresponding config
- Curves represents model evaluation for 5 episodes at 100 step training interval.
- Also, each curve is a mean scores over 5 runs (seeds : [0,100,200,300,400])
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