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Author: simeetnayan81

sota-implementations/a3c/README.md

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+# Reproducing Asynchronous Advantage Actor Critic (A3C) Algorithm Results
+
+This repository contains scripts that enable training agents using the Asynchronous Advantage Actor Critic (A3C) Algorithm on Atari environments. We follow the original paper [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783) by Mnih et al. (2016) to implement the A3C algorithm with a fixed number of steps during the collection phase.
+
+## Examples Structure
+
+Please note that each example is independent of each other for the sake of simplicity. Each example contains the following files:
+
+1. **Main Script:** The definition of algorithm components and the training loop can be found in the main script (e.g. `a3c_atari.py`).
+
+2. **Utils File:** A utility file is provided to contain various helper functions, generally to create the environment and the models (e.g. `utils_atari.py`).
+
+3. **Configuration File:** This file includes default hyperparameters specified in the original paper. Users can modify these hyperparameters to customize their experiments (e.g. `config_atari.yaml`).
+
+## Running the Examples
+
+You can execute the A3C algorithm on Atari environments by running the following command:
+
+```bash
+python a3c_atari.py
+```