openai

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

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

This is more or less similar to what we were doing long time ago with pybullet (#110, #149).

In few words, after #346 the pure-Python gym-ignition package will depend on scenario. When gym-ignition gets installed in a system (currently only Ubuntu is supported), the wheel of scenario will be installed first. Since the wheel of scenario is not self-contained (i.e. it needs to find in t