Seeding RandomMDPEnv

[wessle]

Issue

We need to be able to seed RandomMDPEnv so that, whenever identical seeds are provided, identical MDPEnvs are produced.

Question

Is this already possible with the current class definition?

[wessle]

Doing

from itertools import product
envs = (RandomMDPEnv(10, 10, 'r1', 'c1', transition_seed=1066) for _ in range(10))
Ps = (env.transition_probabilities for env in envs)
P_pairs = product(Ps, Ps)
pairs = product(envs[0].states, envs[0].actions)
all(all(P[0](*pair) == P[1](*pair)) for pair in pairs for P in P_pairs)

returns True, so I think setting transition_seed suffices to guarantee that RandomMDPEnv will always return the same environment.

New Issue

The keyword argument training_seed in RandomMDPEnv appears to be superfluous, since an agent responsible for training should be making the appropriate call to np.seed anyway.

@DavidNKraemer Can we remove training_seed?

[DavidNKraemer]

I would be happy to get rid of the training_seed. I think setting the seed in one place is the best option by far, and have all the downstream consequences flow from that.

[wessle]

Great, and agreed.