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Home Page: https://www.medipixel.io/
License: MIT License
Structural implementation of RL key algorithms
Home Page: https://www.medipixel.io/
License: MIT License
TD3 network 통합
.
gym wrapper로 환경에서 state를 torch.Tensor로 만들어주는 코드 작성
I'd like to play pong with rainbow IQN algorithms, but the repository provides only dqn. How can I play RainbowIQN?
e.g. Callable[[torch.Tensor], torch.Tensor]
mypy 형식으로 수정
@all-contributors please add Curt-Park for code
Now it adjusts the action value ranges by using the first actions values' bound.
We need to consider the case that each action has a different range.
Hi. I'm just wondering why alpha get updated in 'log' space rather than in its own value.
Was doing that stable for entire training? Or any other reason?
# In https://github.com/medipixel/rl_algorithms/blob/master/algorithms/sac/agent.py
# train alpha
if self.hyper_params["AUTO_ENTROPY_TUNING"]:
alpha_loss = (
-self.log_alpha * (log_prob + self.target_entropy).detach()
).mean()
self.alpha_optimizer.zero_grad()
alpha_loss.backward()
self.alpha_optimizer.step()
alpha = self.log_alpha.exp()
else:
alpha_loss = torch.zeros(1)
alpha = self.hyper_params["W_ENTROPY"]
algorithms/bc 디렉토리의 pkl 제거
data/ 디렉토리로 옮겼음
README.md와 각 알고리즘에 대한 문서 작성
50 --> 100 over
추상 클래스를 만들고 상속받는 방식으로 수정
Hello,
I'd like to use pretrained weight for the models (especially dqn.py for Pong), but I don't see one. If it exists, could you provide the link?
Thanks,
Hi, I run example a2c
agent with lunarlander_continuous_v2
env with default hyper parameters for about 80K
episode, but the total score is still about -500
, Could any one help, please. By the way, I suspect it is because the reward is not normalize to mean 0.
[INFO] episode 86207 episode step: 58 total score: -549
total loss: -43826796714054.6172 policy loss: -46156932647123.8594 value loss: 2330135933069.2412
We need check performace TD3 algorithm because not as good as before.
TD3: @MrSyee
SAC: @Curt-Park
TD3: @MrSyee
SAC: @Curt-Park
lgtm is recommended.
pylint 추가
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