README.md

Actor-Critic with Experience Replay and Autocorrelated Actions

This repository contains implementation of Actor-Critic with Experience Replay and autocorrelated actions and Actor-Critic with Experience Replay algorithms.

Installation

Prerequisites

Python3 is required.
Note that, steps bellow won't install all of the OpenAI Gym environments. Visit OpenAI Gym repository for more details.

Installation steps

1. Create new virtual environment:

python3.7 -m venv {name}

Note: it will create the environment folder in your current directory.

2. Activate the virtual environment (should be run from the same directory as above or full path should be passed):

source {name}/bin/activate 

3. While in the repository's root directory, install the requirements:

pip install -r requirements.txt

4. Run the agent:

python run.py {args...}

Example runs

python acerac/run.py --algo acer --env_name Pendulum-v0 --gamma 0.95 \
    --lam 0.9 --b 3 --c 10 --actor_lr 0.001 --learning_starts 1000 --critic_lr 0.002  \
    --actor_layers 20 --critic_layers 50 --memory_size 1000000 \
    --num_parallel_envs 10  --actor_beta_penalty 0.1 --batches_per_env 10

python3.7 acerac/run.py --algo acerac --env_name HalfCheetahBulletEnv-v0 \
    --gamma 0.99 --lam 0.9 --b 2 --learning_starts 1000 --c 1 --actor_lr 0.00003 --critic_lr 0.00006 \
    --actor_layers 256 256  --critic_layers 256 256 --memory_size 1000000 \
    --num_parallel_envs 1 --actor_beta_penalty 0.001 --batches_per_env 256 \
    --num_evaluation_runs 5  --std 0.4  --max_time_steps 3000000 --tau 4 --alpha 0.5

TensorBoard

During the training some statistics like being collected and logged by TensorBoard (logs/ folder). To view the dashboard run

tensorboard --logdir logs

in the repository's root directory. The dashboard will be available in the browser under the addres http://localhost:6006/ To disable storing the logs run the run.py script with --no_tensorboard flag.

References

Marcin Szulc, Jakub Łyskawa, Paweł Wawrzyński,
A framework for reinforcement learning with autocorrelated actions.
ICONIP2020 | paper

Paweł Wawrzyński,
Real-time reinforcement learning by sequential actor–critics and experience replay.
Neural Networks, 22(10):1484–1497, 2009.

Paweł Wawrzyński and Ajay Kumar Tanwani,
Autonomous reinforcement learning with experience replay.
Neural Networks, 41:156-167, 2013.