Comments (3)
V_loss = self.value_criterion(excepted_value, next_value.detach()).mean() # J_V
# Dual Q net
Q1_loss = self.Q1_criterion(excepted_Q1.float(), next_q_value.detach()**.float()**).mean() # J_Q
# Q1_loss = Q1_loss.folat()
Q2_loss = self.Q2_criterion(excepted_Q2.float(), next_q_value.detach().float()).mean()
# Q2_loss = Q2_loss.float()
pi_loss = (log_prob.float() - excepted_new_Q.float()).mean() # according to original paper
from deep-reinforcement-learning-with-pytorch.
1、change all the dtype to float
2、then i met the next bug,the picture is what i had done
from deep-reinforcement-learning-with-pytorch.
How to deal with it?
from deep-reinforcement-learning-with-pytorch.
Related Issues (20)
- A problem in Chapter 5: DDPG HOT 2
- Problem about DDPG
- Cannot solve the Pendulum problem by PPO iml in Chapter 07
- About SAC's version bug (gym 0.17.3) HOT 1
- Some of the code was copied from Morvan
- About PPO HOT 3
- SAC Bugs HOT 3
- About updating.
- Are there TRPO implementations?
- question to author
- about the advantage values in PPO2
- One parameter is missing in DDPG Code HOT 1
- what is your training performance on mujoco?
- Big bug in PPO2 HOT 3
- Temperature factor missing in SAC !!! HOT 1
- bug in reinforce with baseline HOT 2
- wrong code in SAC HOT 1
- Error while seeding! HOT 1
- program error in gridworld.py
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from deep-reinforcement-learning-with-pytorch.