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config.yaml
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config.yaml
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dhc:
cnn_channels: 128
fov: !!python/tuple [9, 9]
observation_radius: 4 # since the FOV is 9x9
observation_shape: !!python/tuple [6, 9, 9]
hidden_dim: 256
max_comm_agents: 3 # includes the agent itself
batch_size: 192
max_num_agents: 16
latent_dim: 784 # 16 * 7 * 7, do not forget to change if the observation_shape is changed
max_episode_length: 256
communication:
disable_communication: 1
comm_enabled_prob: 0.7
num_comm_layers: 2
num_comm_heads: 2
buffer:
action_dim: 5
forward_steps: 2
worker:
episode_capacity: 2048
init_env_settings: !!python/tuple [ 1, 10 ]
max_comm_agents: 3
prioritized_replay_alpha: 0.6
prioritized_replay_beta: 0.4
forward_steps: 2
seq_len: 20
max_map_length: 40
pass_rate: 0.9
learning_starts: 100000
training_times: 600000
target_network_update_freq: 2000
save_interval: 2000
actor_update_steps: 400
train:
num_actors: 16
log_interval: 10
environment:
map_length: 50
num_agents: 2
observation_radius: 4
reward_fn:
move: -0.075
stay_on_goal: 0
stay_off_goal: -0.075
collision: -0.5
finish: 3
init_env_settings: !!python/tuple [1, 10]
observation_shape: !!python/tuple [6, 9, 9]
action_dim: 5