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Further RL documentation

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README.adoc
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@ -72,16 +72,57 @@ Import the package:
using ReCo
----
Run a reinforcement learning process and return the environment helper:
Run a reinforcement learning process and return the environment helper and the the path of the process directory relative to the directory `ReCo.jl`:
[source, julia]
----
env_helper = run_rl(ENVTYPE)
env_helper, rl_dir = run_rl(ENVTYPE)
----
ENVTYPE has to be replaced by one of the environments named after the file names in the directory `ReCo.jl/RL/Envs`, for example: `LocalCOMEnv`. A description of an environment is included at the beginning of the corresponding file.
For more information about all possible optional arguments, press `?` in the REPL after running `using ReCo`. Then type `run_rl` followed by pressing enter.
`env_helper` has the abstract type `EnvHelper`. To access the Q-matrix, enter the following:
[source, julia]
----
env_helper.shared.agent.policy.learner.approximator.table
----
To generate a LaTeX table with the states and actions combintation names for the Q-matrix, run the follwing:
[source, julia]
----
include("src/RL/latex_table.jl")
latex_rl_table(env_helper, FILENAME)
----
FILENAME has to be replaced by the wanted file name of the `.tex` file. This file can then be found under `ReCo.jl/exports/FILENAME`.
To access the rewards, run the following:
[source, julia]
----
env_helper.shared.hook.rewards
----
To plot the rewards, run the following:
[source, julia]
----
plot_rewards(rl_dir)
----
To plot the mean of kappa as the ratio of the eigenvalues of the gyration tensor, run the following:
[source, julia]
----
include("analysis/mean_kappa.jl")
plot_mean_kappa(; rl_dir=rl_dir, n_last_episodes=N_LAST_EPISODES)
----
`N_LAST_EPISODES` is the number of the last episodes of the learning process to average over.
== Run analysis
After running the following command blocks in the REPL, the output can be found in the directory `exports/graphics`.