Initial code release of the BDPI algorithm

.gitignore

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+__pycache__
+out-*
+commands_*
+*.episode

README.md

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+# Sample-Efficient Reinforcement Learning with Bootstrapped Dual Policy Iteration
+
+This repository contains the complete implementation of the Bootstrapped Dual Policy Iteration algorithm we developed over the past year and a half. The repository also contains scripts to re-run and re-plot our experiments.
+
+## Organization
+
+The following components are available in this repository
+
+* The complete Python 3 source code of our algorithm;
+* Two OpenAI Gym environments: Five Rooms and Table (FrozenLake8x8 is available in the OpenAI Gym);
+* Scripts to re-run all our experiments in a fully automated way.
+
+The files are organized as follows:
+
+* `gym_envs/`: Gym environments.
+* `main.py`: A simple RL agent that performs actions in a Gym environment, and learns using BDPI
+* `bdpi.py`: The BDPI learning algorithm (actor, critics, and glue between them)
+* `experiments_gym.sh`: A script that produces a job description for a given environment. Run an experiment with `./experiments_gym.sh table && cat commands_table.sh | parallel -jCORES`
+
+## Dependencies
+
+Reproducing our results require a computer with the following components:
+
+* A recent Linux distribution
+* Python 3, with `lzo` and PyTorch
+* GNU Parallel
+* Gnuplot