rlai/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
rlai/core/__init__.py,sha256=dLYIMbg0uIuXUYV4D0wRfUW1Y-3UELwJHuQA4zAy3Y0,41582
rlai/core/environments/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
rlai/core/environments/bandit.py,sha256=tNz3tXNr3AP5aEHkQWy-S9CmXdODT0KOKST0GoJm78k,6978
rlai/core/environments/gamblers_problem.py,sha256=ZLgwO1HJyJ04h20-nwuRRJNfOcFQxITB52boFP5XQfk,4157
rlai/core/environments/gridworld.py,sha256=vkiL5Crf57pB6BlNkGY_qCeFvmzdrNlY0p-ioC4iUF4,12883
rlai/core/environments/gymnasium.py,sha256=qIjwfKQ4tzMuzQ_-u-y0_pNRt2EQuD_SRxKgE4uCGgs,48381
rlai/core/environments/mancala.py,sha256=nEvNXa0m48BMo0aMHi_dYAM9J1oLoSprgeNCsgpzCNU,14723
rlai/core/environments/mdp.py,sha256=xYdyYXD5Iw0OrBStRSUPxElk4P1AZx-lY6cwTtVjAZ8,27058
rlai/core/environments/network.py,sha256=6N4wCuh90fVwy4o0onSKDQZOlu7_9ahGq7aeQFgoUZY,7025
rlai/core/environments/robocode.py,sha256=hzygH5PVn1_DF9lPQJN9yzpSunBWAne8jVChj7aJSmw,55029
rlai/core/environments/robocode_continuous_action.py,sha256=TLuTy6vq4iW-7F63XA6FBa4xXoYDMjGaCAweXW3dAgc,49209
"rlai/figures/Epsilon-greedy with decreasing epsilon, bandit.pdf",sha256=X4N8pTBNdLiZM8i7eSgWcTThJSiDwlNpNa5yjAHTWKE,69585
"rlai/figures/Epsilon-greedy with recency-weighted reward average, nonstationary bandit.pdf",sha256=4jyhEtOTOual5wtp5__3JRKk2T61p9dCx_DxKvjUizg,70718
"rlai/figures/Epsilon-greedy, nonstationary bandit.pdf",sha256=dYQhpRaMR2wzQF95lg_VUlBjGrdC5XTJX6bNKa6MEXg,73402
"rlai/figures/Figure 2.2: Epsilon-greedy, bandit.pdf",sha256=2ku_ffPs_d22r3s463NAWego5PLS5NlAUqOzRgTATSg,70091
"rlai/figures/Figure 2.3:  Greedy with optimistic initial values and recency-weighted reward average, bandit.pdf",sha256=ncUsUNeizxUiMIRTszo5quFzL5kcYKUdHgCYFA9ECFE,34754
"rlai/figures/Figure 2.4:  Upper confidence bound, bandit.pdf",sha256=kEVPS2nHsFAa3ug0sb66dZ2d1oO22JIBuKvEwTSHwFc,53164
"rlai/figures/Figure 2.5:  Preference gradient no baseline, bandit.pdf",sha256=DgZmsNufc6xrmOKQPrQZn0JyI1IBkW3x9U5Wal4rM3o,33168
"rlai/figures/Figure 2.5:  Preference gradient, bandit.pdf",sha256=tnWf9fd2xCVqdTdjKF7_cXrl0IQcdG0vz_7uHs9OxMg,33473
rlai/figures/Mancala Learning.png,sha256=SmiSh1x42kqh3zeTrJhwCsrXVmPJnF5rDPZvIfabadE,51954
"rlai/figures/Quick run:  Epsilon-greedy, bandit.pdf",sha256=nYzPGfq8URR1Vj0UYvrn_JYOb4CgLTKYSeEhsNFgBcY,32853
rlai/gpi/__init__.py,sha256=fm-hv3PeUJtmTBfNATuwmF6VuJepyCyeMd5ftjWDyRM,384
rlai/gpi/dynamic_programming/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
rlai/gpi/dynamic_programming/evaluation.py,sha256=Iql7ehAmSOGn5YTdnrRZjPSS7TdW3vm9JvPjsYC9x5Q,9402
rlai/gpi/dynamic_programming/iteration.py,sha256=2EzjbgdDqcgwtoR-8AxkgtoGy-EH_0rZQLjpt0vmjPs,6488
rlai/gpi/monte_carlo/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
rlai/gpi/monte_carlo/evaluation.py,sha256=3DXfSLcPCyVlvubC7C7rSzmZUvZn98lxFltUxhHeAsg,13411
rlai/gpi/monte_carlo/iteration.py,sha256=-AdVxfO4IpL8ULcGzwKYKUfLHxKlHv0lISptAVKZ9Uc,7479
rlai/gpi/state_action_value/__init__.py,sha256=_aBAoXzOrdn1AjqR3Xiqq5V-BLNnKtQnDQUvC2I2HUQ,11117
rlai/gpi/state_action_value/function_approximation/__init__.py,sha256=POk9NsKGVHQ-B_S7M66YgDnaBA2dXgqNSDLpfgKruu0,24344
rlai/gpi/state_action_value/function_approximation/models/__init__.py,sha256=D1d3A-15a94ANhkVuhArE_dLCKrSmxuRcUt2siFpGLI,9003
rlai/gpi/state_action_value/function_approximation/models/feature_extraction.py,sha256=GqJDopj5o48YEeI2N3ocyE1cyOekXyJlDmGrTCp-9yc,7955
rlai/gpi/state_action_value/function_approximation/models/sklearn.py,sha256=Bu0MdZplhQVrvP9IwIWhxHXgpuPoiZeBhQP6tjm9k1k,7457
rlai/gpi/state_action_value/tabular.py,sha256=A3MkXIyLsyO0djtARSkfS6ylZbzQFeawQl9kV7oLZS8,22339
rlai/gpi/temporal_difference/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
rlai/gpi/temporal_difference/evaluation.py,sha256=cwTpaFnQ0sLZlqQU7xsgQSFK5C84jRiSubf7-GMBNxc,19379
rlai/gpi/temporal_difference/iteration.py,sha256=aFRoGgtM3LmtgCjEmv7k4wOmlZSUiyUZR2komSp8bz0,8750
rlai/gpi/utils.py,sha256=C2fKGvzcKdbq5b--4XwSEVcpG2lbcqS3xP9NYiLnWQM,9063
rlai/meta.py,sha256=hgyfOG-2PtNLQdqGFywFA7yhtk9NkROhquLGtz0mCXo,7241
rlai/models/__init__.py,sha256=57YTOjwiW_uk0lNFTpjYh2ayppgzkY3Dbvw6bUvYCI4,2587
rlai/models/feature_extraction.py,sha256=WeIFvhxf4btdG1cGN-taZUZQc7NfFonjIQP0hQawCrw,14039
rlai/models/sklearn.py,sha256=I_HvWH6m7wvKL-Q2PN4RHkZMMSEGT5zE4P6KoaZ19Wo,21476
rlai/plot_utils.py,sha256=RzqZO596ZQmFnXsTbPUS-JlZAgAu_v7rNStidxE251k,6732
rlai/policy_gradient/__init__.py,sha256=ygcQTv5AzIv4jlY9fOKcqxpRIDrhHrvlNpWyi99fJF4,3915
rlai/policy_gradient/monte_carlo/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
rlai/policy_gradient/monte_carlo/reinforce.py,sha256=R2nz9Qf-poO4tbolNwl56XRAndPowJP3XHeXk7qNz-8,36205
rlai/policy_gradient/policies/__init__.py,sha256=sYnngTjs0Kg2hIkDY_p4MuvLTg9QtsEboIt62tWYjr0,2396
rlai/policy_gradient/policies/continuous_action.py,sha256=yb8aZmiQ-6CT1DM89bMYwBE0z0sFGgI1CTv1mCBLsw4,36647
rlai/policy_gradient/policies/discrete_action.py,sha256=Gb_TPJDEXWN6QtGldRFdHECLm19gY6pOme8jszJJPzk,15719
rlai/policy_gradient/utils.py,sha256=PYv-YI_qaLBDvEhMg99YlZSBIc6RHlJt9sQ98D30Smk,412
rlai/runners/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
rlai/runners/agent_in_environment.py,sha256=n2RIq1eaqcrJCAFLto_iuz1Wd5UQD5CA4pwZcseGlG4,7010
rlai/runners/top_level.py,sha256=rfCGFT2Ca8yBrQjiQGbrBNf_SzI55ARqkY82WtjY3fk,1592
rlai/runners/trainer.py,sha256=R3SZWysqWNwkimPhB4JYgWVAf8snsM4kvrIqg3J35BA,12816
rlai/state_value/__init__.py,sha256=naZFP8Uu8Lz_9DYGW-lHhbgW7GbIY6sH0jVssYkRnb0,3698
rlai/state_value/function_approximation/__init__.py,sha256=MtAAje9Ia3zZBpAY0oUpUdJ-lJSnvZQkKI0zZEcL5kQ,10438
rlai/state_value/function_approximation/models/__init__.py,sha256=V5WxjiM6f6LJLYBb0HmUGwc85jePNZ_vR85RNU5He1c,1241
rlai/state_value/function_approximation/models/feature_extraction.py,sha256=VxUOGNC4VFyLOiMqoC2xziPy4MwpLMcpxKNUUPD-7vQ,7240
rlai/state_value/function_approximation/models/sklearn.py,sha256=noMYE14fenIdImKTfEF0s9d_G_Ywh3THvQCPOxLDuJw,4190
rlai/utils.py,sha256=FcZFL2ctaM2oAY1Q8gCz16c6FmaDqoNXsV8n_SlkV60,16301
rlai-1.4.0.dist-info/LICENSE,sha256=FF_9fiJxStFD9z5Oskliny-BX6dqSfOwEzpfF-INb-U,1071
rlai-1.4.0.dist-info/METADATA,sha256=-N_N1z3kmOqJhBSFhRanPTxVfApU9RBYSz3aC1nc1XM,2014
rlai-1.4.0.dist-info/WHEEL,sha256=sP946D7jFCHeNz5Iq4fL4Lu-PrWrFsgfLXbbkciIZwg,88
rlai-1.4.0.dist-info/entry_points.txt,sha256=3r91xPzcjjSiL_5IWH_5jL1n_-c5pd1wjdQO2QNXXPg,51
rlai-1.4.0.dist-info/RECORD,,
