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@@ -11,6 +11,7 @@ Implemented algorithms:
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* Async One-Step Q-Learning
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* Async One-Step Sarsa
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* Async N-Step Q-Learning
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* Deep Deterministic Policy Gradient (DDPG)
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# Curves
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> Curves for CartPole are trivial so I didn't place it here.
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@@ -50,5 +51,8 @@ Detailed usage and all training details can be found in ```main.py```
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* [Dueling Network Architectures for Deep Reinforcement Learning](https://arxiv.org/abs/1511.06581)
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* [Playing Atari with Deep Reinforcement Learning](https://arxiv.org/abs/1312.5602)
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* [HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent](https://arxiv.org/abs/1106.5730)
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* [Deterministic Policy Gradient Algorithms](http://proceedings.mlr.press/v32/silver14.pdf)
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* [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)
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* [transedward/pytorch-dqn](https://github.com/transedward/pytorch-dqn)
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* [ikostrikov/pytorch-a3c](https://github.com/ikostrikov/pytorch-a3c)
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* [ghliu/pytorch-ddpg](https://github.com/ghliu/pytorch-ddpg)
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