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ikostrikov / pytorch-a2c-ppo-acktr-gail

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PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

reinforcement-learning deep-learning deep-reinforcement-learning pytorch atari hessian second-order continuous-control actor-critic ale mujoco proximal-policy-optimization ppo advantage-actor-critic a2c acktr natural-gradients roboschool kfac kronecker-factored-approximation
  • Updated Mar 3, 2020
  • Python

rlworkgroup / metaworld

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Benchmarks must be constructed before task names can be accessed

avnishn
avnishn commented Apr 13, 2020

Currently in order to access all of the task names that belong to a benchmark's train/test set, the benchmark and all of its environments must first be constructed.

This is wasteful in the case that one needs to construct a benchmark's environments using the from task_api. The proper behavior should be that one should be able to access the names without constructing all of the underlying enviro

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