Comments (2)
Hi @zsunberg and thanks for the interest. You should probably review #2 and join the gitter chat to get more background on decisions that have been made. Really the design is to allow for maximum flexibility, and easier use than the gym. For example, you can do iteration like:
env = ...
policy = ...
for sars in Episode(env,policy)
# optionally do something?
end
In practice this is much simpler and elegant than the gym api (IMO of course). You can look at the code of OpenAIGym.jl to get a feel of how they relate.
from reinforce.jl.
(continuing on gitter)
from reinforce.jl.
Related Issues (20)
- Interface Consistencies HOT 1
- Finished not exported HOT 1
- Inconsistency in State Type when developing custom Actions HOT 4
- env: add CatePole-v1 HOT 1
- Taking action based on set of states HOT 3
- Support for continuous action space
- Doesn't seem to work in Julia v1.0 HOT 5
- Benchmarks vs tensorflow and pytorch
- example in the exampel folder does not work on Julia v1.0
- Mountain Car Example is not working HOT 2
- Import Plots in Pendulum environment HOT 1
- Add Project.toml HOT 4
- plots not drawn HOT 1
- Documentation
- undefined function in CartPole environment HOT 4
- TagBot trigger issue HOT 1
- Vector of Intervals error message for actions(env, s)
- archive the package? HOT 3
- type annotations
- Register in METADATA HOT 1
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from reinforce.jl.