API

make_env()

It can be used as:

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 env = build_env(EnvName, EnvType)

call_default_params()

It can be used as:

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 alg_params, learn_params = call_default_params(env, EnvType, AlgName)

The call_default_params returns the hyper-parameters stored in two dictionaries alg_params and learn_params, which can be printed to see what are contained inside. Hyper-parameters in these two dictionaries can also be changed by users before instantiating the agent and starting the learning process.

If you want to know exactly where the default hyper-parameters come from, they are stored in an individual Python script as default.py in each algorithm file in ./rlzoo/algorithms/.

alg.learn()

It can be used as:

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 # start the training
 alg.learn(env=env, mode='train', render=False, **learn_params)
 # test after training
 alg.learn(env=env, mode='test', render=True, **learn_params)

where the alg is an instantiation of DRL algorithm in RLzoo.