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ReinforcementLearning-AtariGame
Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games
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README preview
Reinforcement Learning A3C with Gym-Retro
Overview
This project implements the Asynchronous Advantage Actor-Critic (A3C) algorithm with LSTM for scalable reinforcement learning using gym-retro environments. It leverages PyTorch 2 for model development and training.
Features
A3C with LSTM: Enhances the agent's ability to handle partial observability.
Gym-Retro Integration: Supports a wide range of retro games for training.
PyTorch 2 Compatibility: Utilizes the latest PyTorch features for optimal performance.
Multiprocessing Support: Efficiently trains multiple agents in parallel.
Modular Architecture: Clean separation of utilities, models, and agent logic.
Structured Examples: Dedicated training and testing scripts for each game.
The A3C algorithm was released by Google’s DeepMind group earlier this year, and it made a splash by essentially obsoleting DQN. It was faster, simpler, more robust, and able to achieve much better scores on the standard battery of Deep RL tasks. On top of all that it could work in continuous as well as discrete action spaces. Given this, it has become the go-to Deep RL algorithm for new challenging problems with complex state and action spaces
Medium Article explaining A3c reinforcement learning
The Actor-Critic Structure
Many workers training and learning concurrently, and then updates global network with gradients
Process Flow
Long Short Term Memory Recurrent Neural Nets
Implemented using Pytorch
Understanding LSTM Post
Trained models
Trained models are generated when you run through a full training episode for the sim. Continous running will update the model with new training. The L(Load) parameter is set to false in the demo, When you have trained data where it can pick up from, then set it to true.
In gym atari the agents randomly repeat the previous action with probability 0.25 and there is time/step limit that limits performance. You can adjust these parameters.
Optimizers and Shared optimizers/statistics
RMSProp
RMSprop is an unpublished, adaptive learning rate method proposed by Geoff Hinton in Lecture 6e of his Coursera Class.
RMSprop and Adadelta have both been developed independently around the same time stemming from the need to resolve Adagrad's radically diminishing learning rates. RMSprop in fact is identical to the first update vector of Adadelta
RMSprop as well divides the learning rate by an exponentially decaying average of squared gradients. Hinton suggests γ
to be set to 0.9, while a good default value for the learning rate η is 0.001.
Adaptive Moment Estimation (Adam) , Both Shared and non shared available for Adam
is another method that computes adaptive learning rates for each parameter. In addition to storing an exponentially decaying average of past squared gradients vt like Adadelta and RMSprop, Adam also keeps an exponentially decaying average of past gradients mt, similar to momentum:
Adam (short for Adaptive Moment Estimation) is an update to the RMSProp optimizer. In this optimization algorithm, running averages of both the gradients and the second moments of the gradients are used.
It is important to limit number of worker threads to number of cpu cores available in your com.
If you use more than one worker thread than the amount of cpu cores, it will result in poor performance and inefficiency.
Installation
Clone the Repository:
git clone https://github.com/yourusername/reinforcement_learninga3c.git
cd reinforcement_learninga3c
Inside the examples directory, each game has its own subfolder containing train.py and test.py scripts. These scripts are configured to train and test agents on their respective games.
