diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -27,20 +27,34 @@ All code is written in Python 3 and uses RL environments from [OpenAI Gym](https
 ### List of Implemented Algorithms
 
 - [Dynamic Programming Policy Evaluation](DP/Policy%20Evaluation%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FDP%2FPolicy%2520Evaluation%2520Solution.ipynb)
 - [Dynamic Programming Policy Iteration](DP/Policy%20Iteration%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FDP%2FPolicy%2520Iteration%2520Solution.ipynb)
 - [Dynamic Programming Value Iteration](DP/Value%20Iteration%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FDP%2FValue%2520Iteration%2520Solution.ipynb)
 - [Monte Carlo Prediction](MC/MC%20Prediction%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FMC%2FMC%2520Prediction%2520Solution.ipynb)
 - [Monte Carlo Control with Epsilon-Greedy Policies](MC/MC%20Control%20with%20Epsilon-Greedy%20Policies%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FMC%2FMC%2520Control%2520with%2520Epsilon-Greedy%2520Policies%2520Solution.ipynb)
 - [Monte Carlo Off-Policy Control with Importance Sampling](MC/Off-Policy%20MC%20Control%20with%20Weighted%20Importance%20Sampling%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FMC%2FOff-Policy%2520MC%2520Control%2520with%2520Weighted%2520Importance%2520Sampling%2520Solution.ipynb)
 - [SARSA (On Policy TD Learning)](TD/SARSA%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FTD%2FSARSA%2520Solution.ipynb)
 - [Q-Learning (Off Policy TD Learning)](TD/Q-Learning%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FTD%2FQ-Learning%2520Solution.ipynb)
 - [Q-Learning with Linear Function Approximation](FA/Q-Learning%20with%20Value%20Function%20Approximation%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FFA%2FQ-Learning%2520with%2520Value%2520Function%2520Approximation%2520Solution.ipynb)
 - [Deep Q-Learning for Atari Games](DQN/Deep%20Q%20Learning%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FDQN%2FDeep%2520Q%2520Learning%2520Solution.ipynb)
 - [Double Deep-Q Learning for Atari Games](DQN/Double%20DQN%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FDQN%2FDouble%2520DQN%2520Solution.ipynb)
 - Deep Q-Learning with Prioritized Experience Replay (WIP)
 - [Policy Gradient: REINFORCE with Baseline](PolicyGradient/CliffWalk%20REINFORCE%20with%20Baseline%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FPolicyGradient%2FCliffWalk%2520REINFORCE%2520with%2520Baseline%2520Solution.ipynb)
 - [Policy Gradient: Actor Critic with Baseline](PolicyGradient/CliffWalk%20Actor%20Critic%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FPolicyGradient%2FCliffWalk%2520Actor%2520Critic%2520Solution.ipynb)
 - [Policy Gradient: Actor Critic with Baseline for Continuous Action Spaces](PolicyGradient/Continuous%20MountainCar%20Actor%20Critic%20Solution.ipynb)
+ [<img align="right" height="22" src="https://beta.deepnote.org/buttons/launch-in-deepnote.svg">](https://beta.deepnote.org/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fdennybritz%2Freinforcement-learning%2Fblob%2Fmaster%2FPolicyGradient%2FContinuous%2520MountainCar%2520Actor%2520Critic%2520Solution.ipynb)
 - Deterministic Policy Gradients for Continuous Action Spaces (WIP)
 - Deep Deterministic Policy Gradients (DDPG) (WIP)
 - [Asynchronous Advantage Actor Critic (A3C)](PolicyGradient/a3c)