coolguy/coorl-lost-cities
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

Focus project on JAX PPO

Browse Source
This commit is contained in:
coolguy
2026-07-14 20:09:03 +09:00
parent 79273f7eb3
commit ef4b9d82b0
44 changed files with 302 additions and 503 deletions
Download Patch File Download Diff File Expand all files Collapse all files
configs/deep_cfr/default.yaml
-114
View File
@@ -1,114 +0,0 @@
run:
experiment_name: deep-cfr-default
seed: 79
max_iterations: 1000
max_minutes: null
device: cuda
use_amp: false
deterministic: false
rules:
n_colors: 5
n_ranks: 9
min_rank: 2
n_handshakes: 3
hand_size: 8
expedition_penalty: -20
bonus_threshold: 8
bonus_amount: 20
encoding:
derived_playability: true
slot_aware_playability: true
network:
hidden_size: 512
num_layers: 3
activation: relu
traversal:
traversals_per_player: 280
max_depth: null
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.05
outcome_sampling_value_clip: 500.0
outcome_unsampled_regret: zero
cutoff_value_mode: score_diff
cutoff_rollouts: 0
cutoff_rollout_policy: random
cutoff_rollout_max_steps: 300
opponent_policy: average_strategy
strategy_sample_interval: 1
store_strategy_on_traverser_nodes: true
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 64
progress_every_traversals: 0
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
inference_backend: local
scheduler: interleaved
interleave_width: 64
interleave_max_batch: 128
regret_matching:
all_negative_fallback: argmax_tiebreak
training_weighting:
mode: lcfr
self_play:
snapshot_every: 1
max_snapshots: 0
anchor_probability: 0.0
current_weight: 1.0
recent_weight: 0.0
older_weight: 0.0
anchor_weight: 0.0
recent_window: 5
optimization:
advantage_updates_per_iteration: 512
strategy_updates_per_iteration: 512
advantage_batch_size: 1024
strategy_batch_size: 1024
learning_rate: 1.0e-4
weight_decay: 0.0001
grad_clip: 1.0
memory:
advantage_capacity: 2000000
strategy_capacity: 2000000
checkpoint:
save_latest: true
save_every: 50
progress_interval_seconds: 20.0
exact_resume: false
evaluation:
eval_every: 5
games: 100
opponents:
- random
- discard_only
- heuristic_cautious
extended_eval_every: 50
extended_opponents:
- heuristic_balanced
- heuristic_aggressive
- heuristic_noisy
max_steps: 10000
on_max_steps: score_diff
batch_size: 64
device: trainer
num_workers: 4
inference_server:
device: cuda
num_slots: null
max_batch: 256
batch_window_us: 200
weight_sync_every: 1
use_amp: false
configs/deep_cfr/default_server.yaml
-109
View File
@@ -1,109 +0,0 @@
run:
experiment_name: deep-cfr-default-server
seed: 79
max_iterations: 1000
max_minutes: null
device: cuda
use_amp: false
deterministic: false
rules:
n_colors: 5
n_ranks: 9
min_rank: 2
n_handshakes: 3
hand_size: 8
expedition_penalty: -20
bonus_threshold: 8
bonus_amount: 20
encoding:
derived_playability: true
slot_aware_playability: true
network:
hidden_size: 512
num_layers: 3
activation: relu
traversal:
traversals_per_player: 280
max_depth: null
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
outcome_sampling_value_clip: 500.0
outcome_unsampled_regret: zero
cutoff_value_mode: score_diff
cutoff_rollouts: 0
cutoff_rollout_policy: random
cutoff_rollout_max_steps: 300
opponent_policy: average_strategy
strategy_sample_interval: 1
store_strategy_on_traverser_nodes: true
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
inference_backend: server
regret_matching:
all_negative_fallback: argmax_tiebreak
training_weighting:
mode: lcfr
self_play:
snapshot_every: 1
max_snapshots: 0
anchor_probability: 0.0
current_weight: 1.0
recent_weight: 0.0
older_weight: 0.0
anchor_weight: 0.0
recent_window: 5
optimization:
advantage_updates_per_iteration: 512
strategy_updates_per_iteration: 512
advantage_batch_size: 1024
strategy_batch_size: 1024
learning_rate: 1.0e-4
weight_decay: 0.0001
grad_clip: 1.0
memory:
advantage_capacity: 2000000
strategy_capacity: 2000000
checkpoint:
save_latest: true
save_every: 50
progress_interval_seconds: 20.0
exact_resume: false
evaluation:
eval_every: 5
games: 100
opponents:
- random
- discard_only
- heuristic_balanced
- heuristic_aggressive
- heuristic_cautious
- heuristic_noisy
max_steps: 10000
on_max_steps: score_diff
batch_size: 64
device: trainer
num_workers: 4
inference_server:
device: cuda
num_slots: null
max_batch: 256
batch_window_us: 200
weight_sync_every: 1
use_amp: false
configs/deep_cfr/smoke.yaml
-37
View File
@@ -1,37 +0,0 @@
run:
experiment_name: smoke
max_iterations: 1
seed: 1
device: cpu
network:
hidden_size: 16
traversal:
traversals_per_player: 1
max_depth: 2
max_nodes_per_traversal: 64
num_workers: 0
worker_chunk_size: 1
optimization:
advantage_batch_size: 2
strategy_batch_size: 2
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
learning_rate: 0.001
memory:
advantage_capacity: 1000
strategy_capacity: 1000
checkpoint:
save_every: 0
save_latest: false
evaluation:
eval_every: 0
games: 2
opponents:
- random
max_steps: 10000
configs/ismcts/default.yaml
-57
View File
@@ -1,57 +0,0 @@
run:
experiment_name: ismcts-default
max_iterations: 500
seed: 1
device: cuda
rules:
n_colors: 5
n_ranks: 9
min_rank: 2
n_handshakes: 3
hand_size: 8
expedition_penalty: -20
bonus_threshold: 8
bonus_amount: 20
encoding:
derived_playability: true
slot_aware_playability: true
network:
kind: mlp
hidden_size: 768
num_layers: 4
activation: relu
mcts:
n_simulations: 50
c_puct: 5.0
max_depth: 200
parallel_simulations: 64
virtual_loss_value: 5.0
eval_n_simulations: 16
rollout_policy: heuristic_balanced
use_rollout_value: false
root_dirichlet_alpha: 0.3
root_dirichlet_epsilon: 0.4
temperature:
training: 1.0
eval: 0.0
training:
games_per_iter: 10
gradient_steps_per_iter: 10
batch_size: 128
replay_capacity: 100000
interleave_games: 8
interleave_max_batch: 64
num_workers: 8
worker_device: cuda
optimization:
learning_rate: 0.0003
grad_clip: 5.0
checkpoint:
save_every: 20
save_latest: true
evaluation:
eval_every: 5
games: 5
opponents: [random, discard-only, heuristic-cautious]
max_steps: 500
num_workers: 8
configs/ismcts/mini.yaml
-50
View File
@@ -1,50 +0,0 @@
run:
experiment_name: ismcts-mini
max_iterations: 50
seed: 1
device: cpu
rules:
n_colors: 3
n_ranks: 5
min_rank: 2
n_handshakes: 1
hand_size: 4
expedition_penalty: -20
bonus_threshold: 4
bonus_amount: 20
encoding:
derived_playability: true
slot_aware_playability: true
network:
kind: mlp
hidden_size: 128
num_layers: 2
activation: relu
mcts:
n_simulations: 50
c_puct: 1.5
max_depth: 100
parallel_simulations: 8
virtual_loss_value: 1.0
temperature:
training: 1.0
eval: 0.0
training:
games_per_iter: 10
gradient_steps_per_iter: 10
batch_size: 128
replay_capacity: 50000
interleave_games: 8
interleave_max_batch: 64
optimization:
learning_rate: 0.001
grad_clip: 5.0
checkpoint:
save_every: 0
save_latest: true
evaluation:
eval_every: 5
games: 20
opponents: [random, discard-only, heuristic-cautious]
max_steps: 300
num_workers: 1