new reward scheme

This commit is contained in:
Ethan Shapiro 2024-03-18 11:25:14 -07:00
parent bbe9a1891c
commit e799c14ece
3 changed files with 817 additions and 333 deletions

View File

@ -35,240 +35,495 @@
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"Using cpu device\n", "Using cuda device\n",
"Wrapping the env in a DummyVecEnv.\n", "Wrapping the env with a `Monitor` wrapper\n",
"---------------------------------\n", "Wrapping the env in a DummyVecEnv.\n"
"| rollout/ | |\n", ]
"| ep_len_mean | 6 |\n", },
"| ep_rew_mean | 5.59 |\n", {
"| time/ | |\n", "name": "stdout",
"| fps | 544 |\n", "output_type": "stream",
"| iterations | 1 |\n", "text": [
"| time_elapsed | 3 |\n", "----------------------------------\n",
"| total_timesteps | 2048 |\n", "| rollout/ | |\n",
"---------------------------------\n", "| ep_len_mean | 5 |\n",
"-----------------------------------------\n", "| ep_rew_mean | -175 |\n",
"| rollout/ | |\n", "| exploration_rate | 0.525 |\n",
"| ep_len_mean | 6 |\n", "| time/ | |\n",
"| ep_rew_mean | 1.77 |\n", "| episodes | 10000 |\n",
"| time/ | |\n", "| fps | 4606 |\n",
"| fps | 245 |\n", "| time_elapsed | 10 |\n",
"| iterations | 2 |\n", "| total_timesteps | 49989 |\n",
"| time_elapsed | 16 |\n", "----------------------------------\n"
"| total_timesteps | 4096 |\n", ]
"| train/ | |\n", },
"| approx_kl | 0.021515464 |\n", {
"| clip_fraction | 0.335 |\n", "name": "stdout",
"| clip_range | 0.2 |\n", "output_type": "stream",
"| entropy_loss | -9.47 |\n", "text": [
"| explained_variance | 0.00118 |\n", "----------------------------------\n",
"| learning_rate | 0.0003 |\n", "| rollout/ | |\n",
"| loss | 89.5 |\n", "| ep_len_mean | 5 |\n",
"| n_updates | 10 |\n", "| ep_rew_mean | -208 |\n",
"| policy_gradient_loss | -0.0854 |\n", "| exploration_rate | 0.0502 |\n",
"| value_loss | 262 |\n", "| time/ | |\n",
"-----------------------------------------\n", "| episodes | 20000 |\n",
"----------------------------------------\n", "| fps | 1118 |\n",
"| rollout/ | |\n", "| time_elapsed | 89 |\n",
"| ep_len_mean | 6 |\n", "| total_timesteps | 99980 |\n",
"| ep_rew_mean | 1.31 |\n", "| train/ | |\n",
"| time/ | |\n", "| learning_rate | 0.0001 |\n",
"| fps | 211 |\n", "| loss | 24.6 |\n",
"| iterations | 3 |\n", "| n_updates | 12494 |\n",
"| time_elapsed | 29 |\n", "----------------------------------\n"
"| total_timesteps | 6144 |\n", ]
"| train/ | |\n", },
"| approx_kl | 0.02457875 |\n", {
"| clip_fraction | 0.465 |\n", "name": "stdout",
"| clip_range | 0.2 |\n", "output_type": "stream",
"| entropy_loss | -9.47 |\n", "text": [
"| explained_variance | 0.161 |\n", "----------------------------------\n",
"| learning_rate | 0.0003 |\n", "| rollout/ | |\n",
"| loss | 118 |\n", "| ep_len_mean | 5 |\n",
"| n_updates | 20 |\n", "| ep_rew_mean | -230 |\n",
"| policy_gradient_loss | -0.0987 |\n", "| exploration_rate | 0.05 |\n",
"| value_loss | 217 |\n", "| time/ | |\n",
"----------------------------------------\n", "| episodes | 30000 |\n",
"----------------------------------------\n", "| fps | 856 |\n",
"| rollout/ | |\n", "| time_elapsed | 175 |\n",
"| ep_len_mean | 5.96 |\n", "| total_timesteps | 149974 |\n",
"| ep_rew_mean | 5.79 |\n", "| train/ | |\n",
"| time/ | |\n", "| learning_rate | 0.0001 |\n",
"| fps | 196 |\n", "| loss | 18.7 |\n",
"| iterations | 4 |\n", "| n_updates | 24993 |\n",
"| time_elapsed | 41 |\n", "----------------------------------\n"
"| total_timesteps | 8192 |\n", ]
"| train/ | |\n", },
"| approx_kl | 0.02515613 |\n", {
"| clip_fraction | 0.447 |\n", "name": "stdout",
"| clip_range | 0.2 |\n", "output_type": "stream",
"| entropy_loss | -9.47 |\n", "text": [
"| explained_variance | 0.151 |\n", "----------------------------------\n",
"| learning_rate | 0.0003 |\n", "| rollout/ | |\n",
"| loss | 138 |\n", "| ep_len_mean | 5 |\n",
"| n_updates | 30 |\n", "| ep_rew_mean | -242 |\n",
"| policy_gradient_loss | -0.103 |\n", "| exploration_rate | 0.05 |\n",
"| value_loss | 242 |\n", "| time/ | |\n",
"----------------------------------------\n", "| episodes | 40000 |\n",
"-----------------------------------------\n", "| fps | 766 |\n",
"| rollout/ | |\n", "| time_elapsed | 260 |\n",
"| ep_len_mean | 6 |\n", "| total_timesteps | 199967 |\n",
"| ep_rew_mean | 4.9 |\n", "| train/ | |\n",
"| time/ | |\n", "| learning_rate | 0.0001 |\n",
"| fps | 177 |\n", "| loss | 24 |\n",
"| iterations | 5 |\n", "| n_updates | 37491 |\n",
"| time_elapsed | 57 |\n", "----------------------------------\n"
"| total_timesteps | 10240 |\n", ]
"| train/ | |\n", },
"| approx_kl | 0.026685718 |\n", {
"| clip_fraction | 0.444 |\n", "name": "stdout",
"| clip_range | 0.2 |\n", "output_type": "stream",
"| entropy_loss | -9.46 |\n", "text": [
"| explained_variance | 0.176 |\n", "----------------------------------\n",
"| learning_rate | 0.0003 |\n", "| rollout/ | |\n",
"| loss | 96.7 |\n", "| ep_len_mean | 5 |\n",
"| n_updates | 40 |\n", "| ep_rew_mean | -186 |\n",
"| policy_gradient_loss | -0.111 |\n", "| exploration_rate | 0.05 |\n",
"| value_loss | 211 |\n", "| time/ | |\n",
"-----------------------------------------\n", "| episodes | 50000 |\n",
"----------------------------------------\n", "| fps | 722 |\n",
"| rollout/ | |\n", "| time_elapsed | 346 |\n",
"| ep_len_mean | 6 |\n", "| total_timesteps | 249962 |\n",
"| ep_rew_mean | 1.19 |\n", "| train/ | |\n",
"| time/ | |\n", "| learning_rate | 0.0001 |\n",
"| fps | 164 |\n", "| loss | 21.5 |\n",
"| iterations | 6 |\n", "| n_updates | 49990 |\n",
"| time_elapsed | 74 |\n", "----------------------------------\n"
"| total_timesteps | 12288 |\n", ]
"| train/ | |\n", },
"| approx_kl | 0.02762504 |\n", {
"| clip_fraction | 0.463 |\n", "name": "stdout",
"| clip_range | 0.2 |\n", "output_type": "stream",
"| entropy_loss | -9.46 |\n", "text": [
"| explained_variance | 0.186 |\n", "----------------------------------\n",
"| learning_rate | 0.0003 |\n", "| rollout/ | |\n",
"| loss | 103 |\n", "| ep_len_mean | 5 |\n",
"| n_updates | 50 |\n", "| ep_rew_mean | -183 |\n",
"| policy_gradient_loss | -0.115 |\n", "| exploration_rate | 0.05 |\n",
"| value_loss | 200 |\n", "| time/ | |\n",
"----------------------------------------\n", "| episodes | 60000 |\n",
"----------------------------------------\n", "| fps | 694 |\n",
"| rollout/ | |\n", "| time_elapsed | 431 |\n",
"| ep_len_mean | 6 |\n", "| total_timesteps | 299957 |\n",
"| ep_rew_mean | 5.5 |\n", "| train/ | |\n",
"| time/ | |\n", "| learning_rate | 0.0001 |\n",
"| fps | 155 |\n", "| loss | 17.6 |\n",
"| iterations | 7 |\n", "| n_updates | 62489 |\n",
"| time_elapsed | 92 |\n", "----------------------------------\n"
"| total_timesteps | 14336 |\n", ]
"| train/ | |\n", },
"| approx_kl | 0.02694263 |\n", {
"| clip_fraction | 0.458 |\n", "name": "stdout",
"| clip_range | 0.2 |\n", "output_type": "stream",
"| entropy_loss | -9.46 |\n", "text": [
"| explained_variance | 0.15 |\n", "----------------------------------\n",
"| learning_rate | 0.0003 |\n", "| rollout/ | |\n",
"| loss | 84.1 |\n", "| ep_len_mean | 5 |\n",
"| n_updates | 60 |\n", "| ep_rew_mean | -181 |\n",
"| policy_gradient_loss | -0.116 |\n", "| exploration_rate | 0.05 |\n",
"| value_loss | 225 |\n", "| time/ | |\n",
"----------------------------------------\n", "| episodes | 70000 |\n",
"-----------------------------------------\n", "| fps | 675 |\n",
"| rollout/ | |\n", "| time_elapsed | 517 |\n",
"| ep_len_mean | 6 |\n", "| total_timesteps | 349953 |\n",
"| ep_rew_mean | 7.27 |\n", "| train/ | |\n",
"| time/ | |\n", "| learning_rate | 0.0001 |\n",
"| fps | 154 |\n", "| loss | 26.8 |\n",
"| iterations | 8 |\n", "| n_updates | 74988 |\n",
"| time_elapsed | 106 |\n", "----------------------------------\n"
"| total_timesteps | 16384 |\n", ]
"| train/ | |\n", },
"| approx_kl | 0.024316464 |\n", {
"| clip_fraction | 0.412 |\n", "name": "stdout",
"| clip_range | 0.2 |\n", "output_type": "stream",
"| entropy_loss | -9.45 |\n", "text": [
"| explained_variance | 0.173 |\n", "----------------------------------\n",
"| learning_rate | 0.0003 |\n", "| rollout/ | |\n",
"| loss | 126 |\n", "| ep_len_mean | 5 |\n",
"| n_updates | 70 |\n", "| ep_rew_mean | -196 |\n",
"| policy_gradient_loss | -0.112 |\n", "| exploration_rate | 0.05 |\n",
"| value_loss | 227 |\n", "| time/ | |\n",
"-----------------------------------------\n", "| episodes | 80000 |\n",
"-----------------------------------------\n", "| fps | 663 |\n",
"| rollout/ | |\n", "| time_elapsed | 603 |\n",
"| ep_len_mean | 6 |\n", "| total_timesteps | 399936 |\n",
"| ep_rew_mean | 7.8 |\n", "| train/ | |\n",
"| time/ | |\n", "| learning_rate | 0.0001 |\n",
"| fps | 151 |\n", "| loss | 22.5 |\n",
"| iterations | 9 |\n", "| n_updates | 87483 |\n",
"| time_elapsed | 121 |\n", "----------------------------------\n"
"| total_timesteps | 18432 |\n", ]
"| train/ | |\n", },
"| approx_kl | 0.022988513 |\n", {
"| clip_fraction | 0.391 |\n", "name": "stdout",
"| clip_range | 0.2 |\n", "output_type": "stream",
"| entropy_loss | -9.45 |\n", "text": [
"| explained_variance | 0.206 |\n", "----------------------------------\n",
"| learning_rate | 0.0003 |\n", "| rollout/ | |\n",
"| loss | 139 |\n", "| ep_len_mean | 5 |\n",
"| n_updates | 80 |\n", "| ep_rew_mean | -174 |\n",
"| policy_gradient_loss | -0.111 |\n", "| exploration_rate | 0.05 |\n",
"| value_loss | 228 |\n", "| time/ | |\n",
"-----------------------------------------\n", "| episodes | 90000 |\n",
"-----------------------------------------\n", "| fps | 653 |\n",
"| rollout/ | |\n", "| time_elapsed | 688 |\n",
"| ep_len_mean | 6 |\n", "| total_timesteps | 449928 |\n",
"| ep_rew_mean | 6.14 |\n", "| train/ | |\n",
"| time/ | |\n", "| learning_rate | 0.0001 |\n",
"| fps | 153 |\n", "| loss | 21.1 |\n",
"| iterations | 10 |\n", "| n_updates | 99981 |\n",
"| time_elapsed | 133 |\n", "----------------------------------\n"
"| total_timesteps | 20480 |\n", ]
"| train/ | |\n", },
"| approx_kl | 0.022813996 |\n", {
"| clip_fraction | 0.372 |\n", "name": "stdout",
"| clip_range | 0.2 |\n", "output_type": "stream",
"| entropy_loss | -9.45 |\n", "text": [
"| explained_variance | 0.199 |\n", "----------------------------------\n",
"| learning_rate | 0.0003 |\n", "| rollout/ | |\n",
"| loss | 117 |\n", "| ep_len_mean | 5 |\n",
"| n_updates | 90 |\n", "| ep_rew_mean | -155 |\n",
"| policy_gradient_loss | -0.108 |\n", "| exploration_rate | 0.05 |\n",
"| value_loss | 212 |\n", "| time/ | |\n",
"-----------------------------------------\n" "| episodes | 100000 |\n",
"| fps | 645 |\n",
"| time_elapsed | 774 |\n",
"| total_timesteps | 499920 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 22.8 |\n",
"| n_updates | 112479 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -153 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 110000 |\n",
"| fps | 638 |\n",
"| time_elapsed | 860 |\n",
"| total_timesteps | 549916 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 16 |\n",
"| n_updates | 124978 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -164 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 120000 |\n",
"| fps | 633 |\n",
"| time_elapsed | 947 |\n",
"| total_timesteps | 599915 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 17.8 |\n",
"| n_updates | 137478 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -145 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 130000 |\n",
"| fps | 628 |\n",
"| time_elapsed | 1033 |\n",
"| total_timesteps | 649910 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 17.8 |\n",
"| n_updates | 149977 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -154 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 140000 |\n",
"| fps | 624 |\n",
"| time_elapsed | 1120 |\n",
"| total_timesteps | 699902 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 20.9 |\n",
"| n_updates | 162475 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -192 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 150000 |\n",
"| fps | 621 |\n",
"| time_elapsed | 1206 |\n",
"| total_timesteps | 749884 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 18.3 |\n",
"| n_updates | 174970 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -170 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 160000 |\n",
"| fps | 618 |\n",
"| time_elapsed | 1293 |\n",
"| total_timesteps | 799869 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 17.7 |\n",
"| n_updates | 187467 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -233 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 170000 |\n",
"| fps | 615 |\n",
"| time_elapsed | 1380 |\n",
"| total_timesteps | 849855 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 21.6 |\n",
"| n_updates | 199963 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -146 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 180000 |\n",
"| fps | 613 |\n",
"| time_elapsed | 1466 |\n",
"| total_timesteps | 899847 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 19.4 |\n",
"| n_updates | 212461 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -142 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 190000 |\n",
"| fps | 611 |\n",
"| time_elapsed | 1553 |\n",
"| total_timesteps | 949846 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 22.9 |\n",
"| n_updates | 224961 |\n",
"----------------------------------\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 5 |\n",
"| ep_rew_mean | -171 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 200000 |\n",
"| fps | 609 |\n",
"| time_elapsed | 1640 |\n",
"| total_timesteps | 999839 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 20.3 |\n",
"| n_updates | 237459 |\n",
"----------------------------------\n"
] ]
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"source": [ "source": [
"total_timesteps = 20_000\n", "total_timesteps = 1_000_000\n",
"model = PPO(\"MlpPolicy\", env, verbose=1, device='cuda')\n", "model = DQN(\"MlpPolicy\", env, verbose=1, device='cuda')\n",
"model.learn(total_timesteps=total_timesteps)" "model.learn(total_timesteps=total_timesteps, log_interval=10_000, progress_bar=True)"
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"metadata": {}, "metadata": {},
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"source": [ "source": [
"model.save(\"dqn_wordle\")" "model.save(\"dqn_new_rewards\")"
] ]
}, },
{ {
@ -280,88 +535,28 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"c:\\Repository\\cse151b-final-project\\env\\Lib\\site-packages\\stable_baselines3\\common\\save_util.py:166: UserWarning: Could not deserialize object clip_range. Consider using `custom_objects` argument to replace this object.\n", "c:\\Repository\\cse151b-final-project\\env\\Lib\\site-packages\\stable_baselines3\\common\\save_util.py:166: UserWarning: Could not deserialize object lr_schedule. Consider using `custom_objects` argument to replace this object.\n",
"Exception: code() argument 13 must be str, not int\n", "Exception: code() argument 13 must be str, not int\n",
" warnings.warn(\n", " warnings.warn(\n",
"c:\\Repository\\cse151b-final-project\\env\\Lib\\site-packages\\stable_baselines3\\common\\save_util.py:166: UserWarning: Could not deserialize object lr_schedule. Consider using `custom_objects` argument to replace this object.\n", "c:\\Repository\\cse151b-final-project\\env\\Lib\\site-packages\\stable_baselines3\\common\\save_util.py:166: UserWarning: Could not deserialize object exploration_schedule. Consider using `custom_objects` argument to replace this object.\n",
"Exception: code() argument 13 must be str, not int\n", "Exception: code() argument 13 must be str, not int\n",
" warnings.warn(\n" " warnings.warn(\n"
] ]
} }
], ],
"source": [ "source": [
"model = PPO.load(\"dqn_wordle\")" "# model = DQN.load(\"dqn_wordle\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 7,
"metadata": {}, "metadata": {},
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" [18 1 11 5 4 3 3 3 3 3]\n",
" [16 1 9 19 5 3 3 1 1 3]\n",
" [16 1 9 19 5 3 3 1 1 3]\n",
" [16 1 9 19 5 3 3 1 1 3]\n",
" [16 1 9 19 5 3 3 1 1 3]]\n",
"[[16 1 9 19 5 3 3 3 2 1]\n",
" [18 1 11 5 4 3 3 3 2 3]\n",
" [16 1 9 19 5 3 3 3 2 1]\n",
" [16 1 9 19 5 3 3 3 2 1]\n",
" [16 1 9 19 5 3 3 3 2 1]\n",
" [16 1 9 19 5 3 3 3 2 1]]\n",
"[[16 1 9 19 5 3 2 3 3 3]\n",
" [18 8 5 13 5 1 3 3 2 3]\n",
" [16 1 9 19 5 3 2 3 3 3]\n",
" [16 1 9 19 5 3 2 3 3 3]\n",
" [16 1 9 19 5 3 2 3 3 3]\n",
" [16 1 9 19 5 3 2 3 3 3]]\n",
"[[16 1 9 19 5 3 1 3 3 3]\n",
" [18 8 5 13 5 3 3 3 3 3]\n",
" [16 1 9 19 5 3 1 3 3 3]\n",
" [16 1 9 19 5 3 1 3 3 3]\n",
" [16 1 9 19 5 3 1 3 3 3]\n",
" [16 1 9 19 5 3 1 3 3 3]]\n",
"[[16 1 9 19 5 3 3 3 3 2]\n",
" [18 8 5 13 5 3 3 1 1 2]\n",
" [16 1 9 19 5 3 3 3 3 2]\n",
" [16 1 9 19 5 3 3 3 3 2]\n",
" [16 1 9 19 5 3 3 3 3 2]\n",
" [16 1 9 19 5 3 3 3 3 2]]\n",
"0\n" "0\n"
] ]
} }
@ -369,7 +564,7 @@
"source": [ "source": [
"env = gym_wordle.wordle.WordleEnv()\n", "env = gym_wordle.wordle.WordleEnv()\n",
"\n", "\n",
"for i in range(10):\n", "for i in range(1000):\n",
" \n", " \n",
" state, info = env.reset()\n", " state, info = env.reset()\n",
"\n", "\n",
@ -386,16 +581,62 @@
" if info[\"correct\"]:\n", " if info[\"correct\"]:\n",
" wins += 1\n", " wins += 1\n",
"\n", "\n",
"print(wins)\n" "print(wins)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([[18, 1, 20, 5, 19, 3, 3, 3, 3, 3],\n",
" [14, 15, 9, 12, 25, 2, 3, 2, 2, 2],\n",
" [25, 21, 3, 11, 15, 2, 3, 3, 3, 3],\n",
" [25, 21, 3, 11, 15, 2, 3, 3, 3, 3],\n",
" [ 1, 20, 13, 15, 19, 3, 3, 3, 3, 3],\n",
" [25, 21, 3, 11, 15, 2, 3, 3, 3, 3]], dtype=int64),\n",
" -130)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state, reward"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"print()" "blah = (14, 1, 9, 22, 5)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"blah in info['guesses']"
] ]
}, },
{ {

View File

@ -6,6 +6,7 @@ from sty import fg, bg, ef, rs
from collections import Counter from collections import Counter
from gym_wordle.utils import to_english, to_array, get_words from gym_wordle.utils import to_english, to_array, get_words
from typing import Optional from typing import Optional
from collections import defaultdict
class WordList(gym.spaces.Discrete): class WordList(gym.spaces.Discrete):
@ -160,7 +161,14 @@ class WordleEnv(gym.Env):
self.n_rounds = 6 self.n_rounds = 6
self.n_letters = 5 self.n_letters = 5
self.info = {'correct': False, 'guesses': set()} self.info = {
'correct': False,
'guesses': set(),
'known_positions': np.full(5, -1), # -1 for unknown, else letter index
'known_letters': set(), # Letters known to be in the word
'not_in_word': set(), # Letters known not to be in the word
'tried_positions': defaultdict(set) # Positions tried for each letter
}
def _highlighter(self, char: str, flag: int) -> str: def _highlighter(self, char: str, flag: int) -> str:
"""Terminal renderer functionality. Properly highlights a character """Terminal renderer functionality. Properly highlights a character
@ -192,13 +200,57 @@ class WordleEnv(gym.Env):
""" """
self.round = 0 self.round = 0
self.solution = self.solution_space.sample() self.solution = self.solution_space.sample()
self.soln_hash = set(self.solution_space[self.solution])
self.state = np.zeros((self.n_rounds, 2 * self.n_letters), dtype=np.int64) self.state = np.zeros((self.n_rounds, 2 * self.n_letters), dtype=np.int64)
self.info = {'correct': False, 'guesses': set()} self.info = {
'correct': False,
'guesses': set(),
'known_positions': np.full(5, -1),
'known_letters': set(),
'not_in_word': set(),
'tried_positions': defaultdict(set)
}
self.simulate_first_guess()
return self.state, self.info return self.state, self.info
def simulate_first_guess(self):
fixed_first_guess = "rates"
fixed_first_guess_array = to_array(fixed_first_guess)
# Simulate the feedback for each letter in the fixed first guess
feedback = np.zeros(self.n_letters, dtype=int) # Initialize feedback array
for i, letter in enumerate(fixed_first_guess_array):
if letter in self.solution_space[self.solution]:
if letter == self.solution_space[self.solution][i]:
feedback[i] = 1 # Correct position
else:
feedback[i] = 2 # Correct letter, wrong position
else:
feedback[i] = 3 # Letter not in word
# Update the state to reflect the fixed first guess and its feedback
self.state[0, :self.n_letters] = fixed_first_guess_array
self.state[0, self.n_letters:] = feedback
# Update self.info based on the feedback
for i, flag in enumerate(feedback):
if flag == self.right_pos:
# Mark letter as correctly placed
self.info['known_positions'][i] = fixed_first_guess_array[i]
elif flag == self.wrong_pos:
# Note the letter is in the word but in a different position
self.info['known_letters'].add(fixed_first_guess_array[i])
elif flag == self.wrong_char:
# Note the letter is not in the word
self.info['not_in_word'].add(fixed_first_guess_array[i])
# Since we're simulating the first guess, increment the round counter
self.round = 1
def render(self, mode: str = 'human'): def render(self, mode: str = 'human'):
"""Renders the Wordle environment. """Renders the Wordle environment.
@ -220,67 +272,69 @@ class WordleEnv(gym.Env):
super().render(mode=mode) super().render(mode=mode)
def step(self, action): def step(self, action):
"""Run one step of the Wordle game. Every game must be previously
initialized by a call to the `reset` method.
Args:
action: Word guessed by the agent.
Returns:
state (object): Wordle game state after the guess.
reward (float): Reward associated with the guess.
done (bool): Whether the game has ended.
info (dict): Auxiliary diagnostic information.
"""
assert self.action_space.contains(action), 'Invalid word!' assert self.action_space.contains(action), 'Invalid word!'
action = self.action_space[action] guessed_word = self.action_space[action]
solution = self.solution_space[self.solution] solution_word = self.solution_space[self.solution]
self.state[self.round][:self.n_letters] = action reward = 0
correct_guess = np.array_equal(guessed_word, solution_word)
counter = Counter() # Initialize flags for current guess
for i, char in enumerate(action): current_flags = np.full(self.n_letters, self.wrong_char)
flag_i = i + self.n_letters
counter[char] += 1
if char == solution[i]: # Track newly discovered information
self.state[self.round, flag_i] = self.right_pos new_info = False
elif counter[char] <= (char == solution).sum():
self.state[self.round, flag_i] = self.wrong_pos for i in range(self.n_letters):
guessed_letter = guessed_word[i]
if guessed_letter in solution_word:
# Penalize for reusing a letter found to not be in the word
if guessed_letter in self.info['not_in_word']:
reward -= 2
# Handle correct letter in the correct position
if guessed_letter == solution_word[i]:
current_flags[i] = self.right_pos
if self.info['known_positions'][i] != guessed_letter:
reward += 10 # Large reward for new correct placement
new_info = True
self.info['known_positions'][i] = guessed_letter
else:
reward += 20 # Large reward for repeating correct placement
else:
current_flags[i] = self.wrong_pos
if guessed_letter not in self.info['known_letters'] or i not in self.info['tried_positions'][guessed_letter]:
reward += 10 # Reward for guessing a letter in a new position
new_info = True
else:
reward -= 20 # Penalize for not leveraging known information
self.info['known_letters'].add(guessed_letter)
self.info['tried_positions'][guessed_letter].add(i)
else: else:
self.state[self.round, flag_i] = self.wrong_char # New incorrect letter
if guessed_letter not in self.info['not_in_word']:
reward -= 2 # Penalize for guessing a letter not in the word
self.info['not_in_word'].add(guessed_letter)
new_info = True
else:
reward -= 15 # Larger penalty for repeating an incorrect letter
# Update observation state with the current guess and flags
self.state[self.round, :self.n_letters] = guessed_word
self.state[self.round, self.n_letters:] = current_flags
# Check if the game is over
done = self.round == self.n_rounds - 1 or correct_guess
self.info['correct'] = correct_guess
if correct_guess:
reward += 100 # Major reward for winning
elif done:
reward -= 50 # Penalty for losing without using new information effectively
elif not new_info:
reward -= 10 # Penalty if no new information was used in this guess
self.round += 1 self.round += 1
correct = (action == solution).all()
game_over = (self.round == self.n_rounds)
done = correct or game_over
reward = 0
# correct spot
reward += np.sum(self.state[:, 5:] == 1) * 2
# correct letter not correct spot
reward += np.sum(self.state[:, 5:] == 2) * 1
# incorrect letter
reward += np.sum(self.state[:, 5:] == 3) * -1
# guess same word as before
hashable_action = tuple(action)
if hashable_action in self.info['guesses']:
reward += -10
else: # guess different word
reward += 10
self.info['guesses'].add(hashable_action)
# for game ending in win or loss
reward += 10 if correct else -10 if done else 0
self.info['correct'] = correct
# observation, reward, terminated, truncated, info
return self.state, reward, done, False, self.info return self.state, reward, done, False, self.info

189
test.ipynb Normal file
View File

@ -0,0 +1,189 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from collections import defaultdict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def my_func()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"t = defaultdict(lambda: [0, 1, 2, 3, 4])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"defaultdict(<function __main__.<lambda>()>, {})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t['t']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"defaultdict(<function __main__.<lambda>()>, {'t': [0, 1, 2, 3, 4]})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'x' in t"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([1, 1, 1])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"x[:] = 0"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'abcde'aaa\n",
" 33221\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}