From 6f63fc6f44dd1551ab4691c7de9542018115cee5 Mon Sep 17 00:00:00 2001
From: Jun Tian <find_my_way@foxmail.com>
Date: Mon, 25 Jan 2021 23:51:20 +0800
Subject: [PATCH] Update README.md

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 README.md | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/README.md b/README.md
index 0dd1fee..a733d2d 100644
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@@ -4,3 +4,32 @@
 [![CodeCoverage](https://codecov.io/gh/JuliaReinforcementLearning/ReinforcementLearningCore.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaReinforcementLearning/ReinforcementLearningCore.jl)
 
 This package is the core component of [ReinforcementLearning.jl](https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl). It provides some typical implementations of the interfaces defined in [ReinforcementLearningBase.jl](https://github.com/JuliaReinforcementLearning/ReinforcementLearningBase.jl).
+
+
+## Code Structure
+
+```
+./src
+├── core (define how policies interact with environments)
+├── extensions (patch code fore upstream packages are stored here)
+├── policies (all policies are put here)
+│   ├── agents (= policy + trajectory)
+│   ├── q_based_policies
+│   │   ├── explorers (select action from action-values)
+│   │   └── learners (learn state-value, state-action-value, distributional value...)
+│   │       └── approximators (= NN + Optimiser)
+│   └── (some other common policies).jl
+└── utils (Reusable functions/structures)
+```
+
+For developers who are interested in contributing, I suggest you read the source code in the following top-down order:
+
+- `core/run.jl`
+- `policies/base.jl`
+- `policies/agents/agent.jl`
+- `policies/agents/trajectories/trajectory.jl`
+- `policies/q_based_policies/q_based_policy.jl`
+- `policies/q_based_policies/learners/approximators/neural_network_approximator.jl`
+- `policies/q_based_policies/explorers/weighted_explorer.jl`
+
+Then play with the ``E`JuliaRL_BasicDQN_CartPole` `` experiment in [ReinforcementLearningZoo.jl](https://github.com/JuliaReinforcementLearning/ReinforcementLearningZoo.jl) and try to understand the runtime logic step by step. After that, you can explore other components on demand.