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RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploratio...

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Is the OffPolicyAlgorithm now supported, as well as the observation space supported as dict?

Hello, I used the example code provided: https://github.com/yuanmingqi/rl-exploration-baselines/blob/main/examples/ppo_re3_bullet.py to compute intrinsic rewards using ICM module. I found the results were different from what I have got from running mlagents icm...

We want to implement the exploration algorithm into our own developed rl algorithm and a new env with the continuous action. According to our understanding to your code, it seems...

https://github.com/yuanmingqi/rl-exploration-baselines/blob/35d9496affd7afae13873479e33845a90d3583fd/rlexplore/ngu/ngu.py#L54 Hi in this line we set the same variable twice - should it instead be `target_network`?

I noticed that there is no part of training network in the ride code, only two random networks are directly encoded, which seems to be inconsistent with the original paper,...

Hi, First of all thank you for providing these implementations to the community. I've a few questions about your NGU implementation. the original work uses two networks a randomly fixed...

Hi, thanks for the great repository. Is there functionality for testing on custom-user defined envs? If so, how do we do it?

Hi, this looks like a really interesting set of algorithms. I wanted to try some out using the SB3-zoo and was hoping for a plug-and-play approach. I wondered if I...

Hello there Can we save the trained model in this example? Then is it possible to test the model we trained for another environment? How are we going to do?...