<jupyter_start><jupyter_text>Experiment with Tiger Problem<jupyter_code>import numpy as np
import os
import tensorflow.compat.v1 as tf
save_path = './rollout_exp/Tiger'
save_str = 'LAN-{}-{}.pkl'
log_str = 'log_LAN-{}-{}.txt'<jupyter_output><empty_output><jupyter_text>baseline<jupyter_code>experiment = 'baseline'
%%capture cap --no-stderr
tf.reset_default_graph()
i = 1
seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))    
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name 
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)
i+=1

tf.reset_default_graph()
%%capture cap --no-stderr

seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name 
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)
i+=1

tf.reset_default_graph()
%%capture cap --no-stderr

seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i)) 
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name 
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)<jupyter_output><empty_output><jupyter_text>deceptive kernel - rand<jupyter_code>experiment = 'rand'
%%capture cap --no-stderr
i = 1
tf.reset_default_graph()
seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))    
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name --k_type rand
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)
i+=1

tf.reset_default_graph()
%%capture cap --no-stderr

seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))   
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name --k_type rand
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)
i+=1

tf.reset_default_graph()
%%capture cap --no-stderr

seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))  
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name --k_type rand
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)<jupyter_output><empty_output><jupyter_text>prob<jupyter_code>experiment = 'prob'
%%capture cap --no-stderr
tf.reset_default_graph()
i = 1
seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))   
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name --k_type prob --d_prob 0.75
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)
i+=1

tf.reset_default_graph()
%%capture cap --no-stderr

seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name --k_type prob --d_prob 0.75
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)
i+=1

tf.reset_default_graph()
%%capture cap --no-stderr

seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name --k_type prob --d_prob 0.75
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)<jupyter_output><empty_output><jupyter_text>oppo<jupyter_code>experiment = 'oppo'
%%capture cap --no-stderr
tf.reset_default_graph()
i = 1
seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name --k_type oppo
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)
i+=1

tf.reset_default_graph()
%%capture cap --no-stderr

seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name --k_type oppo
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)
i+=1

tf.reset_default_graph()
%%capture cap --no-stderr

seed = np.random.randint(10e5)
save_name = save_str.format(experiment, i)

print("Experiment: {}; Run: {}\n".format(experiment, i))
%run vi.py --env Tiger --solver LinearAlphaNet --use_tf --n_epochs 5000 --max_steps 50 --test 10 --learning_rate 0.05 --learning_rate_decay 0.996 --learning_rate_minimum 0.00025 --learning_rate_decay_step 50 --beta 0.001 --epsilon_start 0.2 --epsilon_minimum 0.02 --epsilon_decay 0.99 --epsilon_decay_step 75 --seed $seed --save --save_name $save_name --k_type oppo
log = log_str.format(experiment, i)
log_path = os.path.join(save_path, log)

with open(log_path, 'w') as f:
    f.write(cap.stdout)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15251182698806037, 'fraction_numerical': 0.056544266726740254, 'mean_word_length': 4.186331775700935, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '67255', 'license': 'MIT', 'n_tokens_mistral': 3726, 'n_tokens_neox': 3311, 'n_words': 797, 'path': 'experiment.ipynb', 'repo_name': 'joshzzl/POMDPy', 'size': '8878'}