This repo contains code accompaning the paper, Meta-Reinforcement Learning of Structured Exploration Strategies (Gupta et al., NIPS 2018).
This code is based off of the rllab code repository and can be installed in the same way (see below). This codebase is not necessarily backwards compatible with rllab.
The MAESN code uses the TensorFlow rllab version, so be sure to install TensorFlow v1.0+.
Scripts for running the experiments found in the paper are located in launchers/. The scripts are setup to use local_docker or ec2. The MuJoCo environments are located in rllab/envs/mujoco/.
python train.py <algo> --env <envName>, where algo can be Maesn or LSBaseline, envName can be Ant, Pusher or Wheeled.
Hyperparameters can be set in the hyperparam_sweep file. For the LSBaseline, the fast_learning_rate must be 0.
python test.py <algo> --env <envName> --initial_params_file <fileName> --learning_rate <rate> --latent_dim <latentDim>.
The meta-trained policy file must be placed in metaTrainedPolices/, and its name is then passed to initial_params_file.
Make sure that the rate and latentDim at testing are the same as the fast_learning_rate and latent_dim at training.
The following image IDs are known to work
us-east-1 : ami-0bb2bf4857db440e0
us-west-1 : ami-05e00979871a39188
us-west-2 : ami-0111a16ab53cb7ea9
Docker Image : russellm888/rllab3
rllab is a framework for developing and evaluating reinforcement learning algorithms. It includes a wide range of continuous control tasks plus implementations of the following algorithms:
- REINFORCE
- Truncated Natural Policy Gradient
- Reward-Weighted Regression
- Relative Entropy Policy Search
- Trust Region Policy Optimization
- Cross Entropy Method
- Covariance Matrix Adaption Evolution Strategy
- Deep Deterministic Policy Gradient
rllab is fully compatible with OpenAI Gym. See here for instructions and examples.
rllab only officially supports Python 3.5+. For an older snapshot of rllab sitting on Python 2, please use the py2 branch.
rllab comes with support for running reinforcement learning experiments on an EC2 cluster, and tools for visualizing the results. See the documentation for details.
The main modules use Theano as the underlying framework, and we have support for TensorFlow under sandbox/rocky/tf.
Documentation is available online: https://rllab.readthedocs.org/en/latest/.
If you use rllab for academic research, you are highly encouraged to cite the following paper:
- Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel. "Benchmarking Deep Reinforcement Learning for Continuous Control". Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016.
rllab was originally developed by Rocky Duan (UC Berkeley / OpenAI), Peter Chen (UC Berkeley), Rein Houthooft (UC Berkeley / OpenAI), John Schulman (UC Berkeley / OpenAI), and Pieter Abbeel (UC Berkeley / OpenAI). The library is continued to be jointly developed by people at OpenAI and UC Berkeley.
Slides presented at ICML 2016: https://www.dropbox.com/s/rqtpp1jv2jtzxeg/ICML2016_benchmarking_slides.pdf?dl=0