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Distributional Reinforcement Learning

This repository is by Pierre-Alexandre K. and Paul-Ambroise D. and contains the PyTorch source code to reproduce the results of Bellemare and al. ["A Distributional Perspective on Reinforcement Learning"](https://arxiv.org/abs/1707.06887).

Requirements

- Python 3.6 - Torch - OpenAI gym

Results

We used the categorical algorithm to solve [CartPole-v0](https://gym.openai.com/envs/CartPole-v0/).

The following results were not optimized over different hyperparameters, so there is room for improvement.

The evolution of the distribution for the [0, 0, 0, 0] state is the following:

Discussion

We want to extend the work of Bellemare and al. to continuous action using either ICNN, CEM or NAF to handle continuous actions. An ICNN implementation is yet available but needs optimization.

Implicit : étendre aux actions continues https://arxiv.org/pdf/1806.06923.pdf QUOTA : https://arxiv.org/pdf/1811.02073.pdf Quantile regression : c51 qrdqn DISTRIBUTED DISTRIBUTIONAL DETERMINISTIC POLICY GRADIENTS: https://openreview.net/pdf?id=SyZipzbCb

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