Trainable Beta 1.0

The code is now stable enough to use. There are four different ways to train:

Training Commands

Basic Training (vs. Random Agents)

Train a new agent against random opponents:

python train.py --iterations 1000 --traversals 200

Continue Training from Checkpoint

Resume training from a saved checkpoint against random opponents:

python train.py --checkpoint models/checkpoint_iter_1000.pt --iterations 1000

Self-Play Training

Train against a fixed checkpoint opponent:

python train.py --checkpoint models/checkpoint_iter_1000.pt --self-play --iterations 1000

Mixed Checkpoint Training

Train against a rotating pool of checkpoint opponents:

python train.py --mixed --checkpoint-dir models --model-prefix t_ --refresh-interval 1000 --num-opponents 5 --iterations 10000

Continue an existing agent with mixed checkpoint training:

python train.py --mixed --checkpoint models/checkpoint_iter_1000.pt --checkpoint-dir models --model-prefix t_ --iterations 1000

Parameters

Parameter	Description	Default
--iterations	Number of training iterations	1000
--traversals	Number of game traversals per iteration	200
--save-dir	Directory to save model checkpoints	"models"
--log-dir	Directory for TensorBoard logs	"logs/deepcfr"
--checkpoint	Path to a checkpoint file to continue training	None
--verbose	Enable detailed output	False
--self-play	Train against a checkpoint instead of random agents	False
--mixed	Use mixed checkpoint training	False
--checkpoint-dir	Directory containing checkpoint models for mixed training	"models"
--model-prefix	Prefix for models to include in the mixed training pool	"t_"
--refresh-interval	How often to refresh opponents in mixed training	1000
--num-opponents	Number of checkpoint opponents to use in mixed training	5

Training Strategies

Random Opponent Training

• Fastest training method
• Good for initial learning
• Agent may overfit to exploit random play

Self-Play Training

• Trains against a fixed strong opponent
• Helps develop more balanced strategies
• May develop specific counter-strategies against the opponent

Mixed Checkpoint Training

• Most robust training method
• Prevents overfitting to specific opponent types
• Provides diverse learning experiences
• Closest approximation to Nash equilibrium training

Monitoring Training

Monitor training progress with TensorBoard:

tensorboard --logdir=logs

Then open http://localhost:6006 in your browser.