-scenario= train_mace
-output_path= output/goat_mace3_model.h5

-character_file= data/characters/goat.txt
-state_file= data/states/goat_state_climb.txt
-char_init_pos_x= 1.2

-num_threads= 4

-char_type= dog
-char_ctrl= goat_mace
-terrain_file= data/terrain/cliffs_rugged.txt

-policy_solver= data/policies/dog/nets/dog_mace3_solver.prototxt
-policy_net= data/policies/dog/nets/dog_mace3_deploy.prototxt

-trainer_num_anneal_iters= 50000
-exp_base_anneal_iters= 50000
-trainer_curriculum_iters= 0

-exp_layer= ip0
-exp_rate= 0.2
-exp_temp= 0.025
-exp_base_rate= 0.002
-init_exp_rate= 0.9
-init_exp_temp= 20
-init_exp_base_rate= 0.9

-trainer_replay_mem_size= 500000
-trainer_max_iter= 1000000000
-trainer_freeze_target_iters= 500
-trainer_int_iter= 2000
-trainer_int_output= output/intermediate/trainer_int_model.h5
-tuple_buffer_size= 32
-trainer_num_init_samples= 50000
-trainer_num_steps_per_iters= 1
-trainer_iters_per_output= 200
-trainer_init_input_offset_scale= true
-trainer_enable_async_mode= false