gym

The following applies to DDPG and TD3, and possibly other models. The following libraries were installed in a virtual environment:

numpy==1.16.4
stable-baselines==2.10.0
gym==0.14.0
tensorflow==1.14.0

Episode rewards do not seem to be updated in model.learn() before callback.on_step(). Depending on which callback.locals variable is used, this means that:

• episode rewards may n

I'll post it as a question as I am not quite sure that it is a bug. I have been experimenting for a while with the library in a custom environment for a school project and I am really interested in the reproducibility of the result. I have read the disclaimer in the documentation that reads that reproducible results are not guaranteed across multiple platforms or different versions of Pytorch. Ho

I would love for Wger to become the central database that all FOSS projects pull workout information from. However, workout apps tend to use very different art styles — wger itself uses line art, openWorkout uses 3D animation, Feeel uses low-poly images.

It'd be great if volunteers could upload graphics in any o

Per this comment in #12

Problem Description

Procgen Environments (https://github.com/openai/procgen) are new environments to test out the generalization ability of agents. It would be nice to include some of the games into the Open RL Benchmark (http://benchmark.cleanrl.dev/)

This is a good first issue for contributors. I think contributors can simply modify the network model slightly (https://github.com/vwxyzjn/c

There seem to be some vulnerabilities in our code that might fail easily. I suggest adding more unit tests for the following:

• Custom agents (there's only VPG and PPO on CartPole-v0 as of now. We should preferably add more to cover discrete-offpolicy, continuous-offpolicy and continuous-onpolicy)
• Evaluation for the Bandits and Classical agents
• Testing of convergence of agents as proposed i