Comments (7)
Agreed. We can go with RAINBOW.
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MuZero would also be a cool algorithm, it is a bit more complicated with the MCTS but it works very well
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Hi!
I think A2C (synchronous update version of A3C) is good.
What about implementing RAINBOW rather than Double, Dueling DQN?
I think the significance of the code to both Double and Dueling DQN is marginal because they are small variations of DQN in terms of implementation.
In contrast, a simple implementation of the RAINBOW might be helpful for many people.
(Actually, Dueling and Double DQN are 2 components of RAINBOW out of 6)
https://arxiv.org/abs/1710.02298
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Awesome!
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Also, thanks so much for sharing.
These are great simple implementations for learning and have been very useful.
If you want to try something else, you could also try to implement them in TensorFlow
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How about SAC?
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How about Phasic Policy Gradient (PPG) as it gives better results than PPO?
Also an example of using these algorithms for non gaming environment like ones with list, dict etc as observation instead of image frames. I guess that will be easy as we will have to use NN instead of CNN. Still a simple example, may be.
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Related Issues (20)
- RuntimeError while running DDPG.py HOT 2
- PPO update mistake? HOT 1
- TD3: Twin Delayed DDPG HOT 2
- Soft Actor Critic? HOT 1
- Use pytorch-lightning for better readability and optimization
- Maybe a bug in SAC Implementation? HOT 1
- cartpole ppo train , reward drop HOT 1
- Remove unused import HOT 1
- A naive question about updating parameters in DDPG.
- Cartpole environment with Multidiscrete action space HOT 3
- Cartpole environment with Multidiscrete action space
- Add meta RL algorithms?
- Query about LSTM
- Add minimal IMPALA? HOT 2
- Minimal way to save / replay trained model?
- MuZero minimal implementation
- DQN why train iterate for 10 times
- TypeError: expected np.ndarray (got tuple) HOT 1
- Training speed is very slow!!! HOT 1
- Wrong formula for calc-target in SAC?
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