Update README.md

README.md

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 # Addressing Function Approximation Error in Actor-Critic Methods
-Scott Fujimoto, Herke van Hoof and David Meger
 
-PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3). If you use our code or data please cite the paper: []
+PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3). If you use our code or data please cite the [paper]().
 
 Method is tested on [MuJoCo](http://www.mujoco.org/) continuous control tasks in [OpenAI gym](https://github.com/openai/gym). 
 Networks are trained using [PyTorch](https://github.com/pytorch/pytorch). 
 
+### Usage
 The paper results can be reproduced exactly running the experiments.sh script. 
 Experiments on single environments can be run by calling
 ```
@@ -16,4 +16,7 @@ Hyper-parameters can be modified with different arguments to main.py. We include
 
 Algorithms which TD3 compares against (PPO, TRPO, ACKTR, DDPG) can be found at [OpenAI baselines repository](https://github.com/openai/baselines). 
 
+### Results
 Learning curves found in the paper are found under /learning_curves. Each learning curve are formatted as NumPy arrays of 201 evaluations (201,), where each evaluation corresponds to the average total reward from running the policy for 10 episodes with no exploration. The first evaluation is the randomly initialized policy network (unused in the paper). 
+
+Numerical results can be found in the paper, or from the learning curves. Video of the learned agent can be found [here]().