Collaborative Evolutionary Reinforcement Learning for NIPS AI for Prosthetics Challenge 2018
################################# Code labels #################################
main.py: Neureovolution learner that generates data --> Data Storage and bootstraps off policies from policy storage
full_pg.py: Off-policy policy gradient trainer using (Advantage) DDPG/TD3 with/or without Trust Region constraints and HER
expert_q.py: Expert Agent that uses an ensemble of critics and actors to take expert decisions (simiular to a simple MPC but using critics instead of a model)
train_vae.py: Trains a Variational Autoencoder Forward Model using data from data storage
core/runner.py: Rollout worker
core/env_wrapper.py: Wrapper around the Prosthetics Opensim env exposing a cleaner interface for RL algo
core/her.py: Implements Hindsight Experience Replay
core/models.py: Actor model
core/neuroevolution.py: Implements Sub-Structured Based Neuroevolution (SSNE) with a dynamic population
core/off_policy_gradient.py: Implements the off_policy_gradient learner (TD3/DDPG) with/or without Advantage functions, Trust Regions and HER
core/reward_shaping.py: Implements Behavioral Reward shaping spanning dynamic/static trajectory-wide shaping functions with and without relaxations
core/ounoise.py: Implements Ornstein–Uhlenbeck process for generating temporally correlated noise
core/vae_fm.py: Variational Autoencoder based Forward model with Inverse kinematic losses
core/mod_utils.py: Helper functions
###################################### Auxiliary scripts: ######################################
viz.py: Visualize policies (rendering)
score_net.py: Benchmark policies without rendering
submission.py: Submit a policy to the AI for Prosthetics server for scoring
q_test.py: Test the q-values encoded by the critic
action_cluster.py: Cluster the actions generated by the actors and analyze them (WIP)
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