Comments (2)
dnddnjs
commented on May 18, 2024
Thank you for utilizing our code for your own code. We will review your code and tell you our opinion!
from reinforcement-learning.
codetiger
commented on May 18, 2024
Hi, I added some optimization techniques to my Agent and got better results.
The agent was trained for 100K episodes in 2x2 grid and got 100% optimal move every time. However, I did not have enough patience to train the agent for 4x4 grid. Updated my repo with new results
from reinforcement-learning.
Related Issues (20)
- 5_A3C Cartpole Script - AttributeError: 'Functional' object has no attribute '_make_predict_function' HOT 4
- Variable Tensor("Neg:0", shape=(), dtype=float32) has `None` for gradient. HOT 1
- How to run this example code?
- Cartpole Policy Gradient script does not converge (2-cartpole/3-reinforce/cartpole_reinforce.py)
- Question on Policy Gradient
- Pong Policy Gradient-important error in the definition of the convolutional net. HOT 1
- A3C on GPU
- rlcode.github.io does not exist !
- is it possible to apply categorical_crossentropy to a3c? HOT 1
- How to add Dropout
- update target_model before loading saved model in cartpole_dqn.py
- Implementing policy gradient when number of output classes is large
- Dqn-per does not use importance sampling weight in training。
- reinforcement learning real life use cases
- The issue about breakout_a3c.py in 3-atari, when i execute source HOT 1
- Why are you using SARSA instead of Q-Learning? HOT 1
- issue regarding saved models
- How to run threading while using Keras and tensorflow
- Can this code run other atari game beside breakout?
- Diagonal movement? - Grid Score
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