AlphaZero in JAX using PAX library.
pip install -r requirements.txtpython train_agent.pyTF_CPP_MIN_LOG_LEVEL=2 \
python train_agent.py \
--game_class="connect_four_game.Connect4Game" \
--agent_class="resnet_policy.ResnetPolicyValueNet" \
--batch-size=4096 \
--num_simulations_per_move=32 \
--num_self_plays_per_iteration=102400 \
--learning-rate=1e-2 \
--num_iterations=500 \
--lr-decay-steps=200000A live Connect-4 agent is running at https://huggingface.co/spaces/ntt123/Connect-4-Game. We use tensorflow.js to run the policy on the browser.
TF_CPP_MIN_LOG_LEVEL=2 \
python3 train_agent.py \
--game-class="caro_game.CaroGame" \
--agent-class="resnet_policy.ResnetPolicyValueNet128" \
--selfplay-batch-size=1024 \
--training-batch-size=1024 \
--num-simulations-per-move=32 \
--num-self-plays-per-iteration=102400 \
--learning-rate=1e-2 \
--random-seed=42 \
--ckpt-filename="./caro_agent_9x9_128.ckpt" \
--num-iterations=100 \
--lr-decay-steps=500000A live Caro agent is running at https://caro.ntt123.repl.co.
TF_CPP_MIN_LOG_LEVEL=2 \
python3 train_agent.py \
--game-class="go_game.GoBoard9x9" \
--agent-class="resnet_policy.ResnetPolicyValueNet128" \
--selfplay-batch-size=1024 \
--training-batch-size=1024 \
--num-simulations-per-move=32 \
--num-self-plays-per-iteration=102400 \
--learning-rate=1e-2 \
--random-seed=42 \
--ckpt-filename="./go_agent_9x9_128.ckpt" \
--num-iterations=200 \
--lr-decay-steps=1000000A live Go agent is running at https://go.ntt123.repl.co.
You can run the agent on your local machine with the go_web_app.py script.
We also have an interative colab notebook that runs the agent on GPU to reduce inference time.
python plot_search_tree.py
# ./search_tree.pngpython play.pyAgents in the above demos are trained on Google TPUs sponsored by Google under the TPU Research Cloud program.
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