add sample introduction

docs/source/get_started/introduction.rst

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 Introduction by Example
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+=======================
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+Welcome to the introduction of **gflownet**, a library designed to facilitate training and extending Generative Flow Networks (GFlowNets) for a wide range of applications, especially in scientific discovery. GFlowNets provide a framework for probabilistic and generative modeling.
+
+What are GFlowNets?
+-------------------
+
+Generative Flow Networks, or GFlowNets, are a type of generative model that sample objects $x \in \mathcal{X}$ proportionally to a reward function $R(x)$. This framework is particularly suited for applications where the goal is to generate diverse high-reward samples, such as in scientific discovery.
+
+.. image:: ../figures/reward_landscape.png
+   :align: center
+   :width: 400
+   :alt: Reward landscape
+
+The model learns to transition between states in a way that the frequency of generating any given state is proportional to its reward. For instance, a GFlowNet trained on a 2D space with four high-reward areas would learn to sample those areas more frequently.
+
+Compositionality and Sampling
+------------------------------
+
+GFlowNets utilize compositionality by decomposing samples $x$ into sequences of intermediate states, such as:
+
+.. image:: ../figures/tetris_flows.png
+   :align: center
+   :width: 400
+   :alt: Tetris flows
+
+In the example of generating Tetris-like boards, each board configuration is built by adding one piece at a time, starting from an empty board. Each intermediate state has potential "parents" and "children," forming a directed graph over states, where each edge (transition from one state to another) is modeled and learned.
+
+
+Key Components
+--------------
+
+**gflownet** comprises several core components essential for its operation:
+
+- **Environment**: Defines the state and action spaces for tasks, such as the Tetris or Scrabble environments.
+- **Proxy**: Manages the reward function logic, essential for evaluating states.
+- **Policy Models**: Neural networks that model the transitions between states.
+- **GFlowNet Agent**: Orchestrates interactions between the environment, policies, and proxy, among other functions.