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Documentation update that fixes typo REINFROCE to REINFORCE (#672)

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Zarif Aziz
2023-08-18 10:23:39 +10:00
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docs/tutorials/training_agents/reinforce_invpend_gym_v26.py
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@@ -135,7 +135,7 @@ class Policy_Network(nn.Module):
# Now that we are done building the policy, let us develop **REINFORCE** which gives life to the policy network.
# The algorithm of REINFORCE could be found above. As mentioned before, REINFORCE aims to maximize the Monte-Carlo returns.
#
# Fun Fact: REINFROCE is an acronym for " 'RE'ward 'I'ncrement 'N'on-negative 'F'actor times 'O'ffset 'R'einforcement times 'C'haracteristic 'E'ligibility
# Fun Fact: REINFORCE is an acronym for " 'RE'ward 'I'ncrement 'N'on-negative 'F'actor times 'O'ffset 'R'einforcement times 'C'haracteristic 'E'ligibility
#
# Note: The choice of hyperparameters is to train a decently performing agent. No extensive hyperparameter
# tuning was done.