ACER

by Kaixhin

Kaixhin / ACER

Actor-critic with experience replay

205 Stars 39 Forks Last release: Not found MIT License 99 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

ACER

MIT License

Actor-critic with experience replay (ACER) [1]. Uses batch off-policy updates to improve stability. Trust region updates can be enabled with

--trust-region
. Currently uses full trust region instead of "efficient" trust region (see issue #1).

Run with

python main.py 
. To run asynchronous advantage actor-critic (A3C) [2] (but with a Q-value head), use the
--on-policy
option.

Requirements

To install all dependencies with Anaconda run

conda env create -f environment.yml
and use
source activate acer
to activate the environment.

Results

ACER

Acknowledgements

References

[1] Sample Efficient Actor-Critic with Experience Replay
[2] Asynchronous Methods for Deep Reinforcement Learning

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.