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🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!

Python 60.93% CMake 3.90% C++ 9.80% C 4.55% Makefile 20.59% Shell 0.23%
deep-reinforcement-learning keras pytorch tensorflow convolutional-neural-networks lstm dqn ddpg actor-critic ppo

machine-learning-is-all-you-need's Issues

the implement of TD error in Actor_Critic

Machine-Learning-is-ALL-You-Need/RL_Actor_Critic/17Actor_Critic.py

Lines 80 to 92 in 766a50b

R = 0
saved_actions = self.model.saved_actions
policy_losses = [] # list to save actor (policy) loss
value_losses = [] # list to save critic (value) loss
returns = [] # list to save the true values
# calculate the true value using rewards returned from the environment
for r in self.model.rewards[::-1]:
# calculate the discounted value
R = r + self.gamma * R
returns.insert(0, R)
returns = torch.tensor(returns)

Shouldn't R here be set as the value estimation of the last state?
If I understand correctly, if R is set to 0, the returns for the later t become less accurate, and it also fails to reflect the temporal difference.

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