TensorFlow implementation of Deep Reinforcement Learning papers

Last update: Jan 03, 2023

Overview

Deep Reinforcement Learning in TensorFlow

TensorFlow implementation of Deep Reinforcement Learning papers. This implementation contains:

[1] Playing Atari with Deep Reinforcement Learning
[2] Human-Level Control through Deep Reinforcement Learning
[3] Deep Reinforcement Learning with Double Q-learning
[4] Dueling Network Architectures for Deep Reinforcement Learning
[5] Prioritized Experience Replay (in progress)
[6] Deep Exploration via Bootstrapped DQN (in progress)
[7] Asynchronous Methods for Deep Reinforcement Learning (in progress)
[8] Continuous Deep q-Learning with Model-based Acceleration (in progress)

Requirements

Usage

First, install prerequisites with:

$ pip install -U 'gym[all]' tqdm scipy

Don't forget to also install the latest TensorFlow. Also note that you need to install the dependences of doom-py which is required by gym[all]

Train with DQN model described in [1] without gpu:

$ python main.py --network_header_type=nips --env_name=Breakout-v0 --use_gpu=False

Train with DQN model described in [2]:

$ python main.py --network_header_type=nature --env_name=Breakout-v0

Train with Double DQN model described in [3]:

$ python main.py --double_q=True --env_name=Breakout-v0

Train with Deuling network with Double Q-learning described in [4]:

$ python main.py --double_q=True --network_output_type=dueling --env_name=Breakout-v0

Train with MLP model described in [4] with corridor environment (useful for debugging):

$ python main.py --network_header_type=mlp --network_output_type=normal --observation_dims='[16]' --env_name=CorridorSmall-v5 --t_learn_start=0.1 --learning_rate_decay_step=0.1 --history_length=1 --n_action_repeat=1 --t_ep_end=10 --display=True --learning_rate=0.025 --learning_rate_minimum=0.0025
$ python main.py --network_header_type=mlp --network_output_type=normal --double_q=True --observation_dims='[16]' --env_name=CorridorSmall-v5 --t_learn_start=0.1 --learning_rate_decay_step=0.1 --history_length=1 --n_action_repeat=1 --t_ep_end=10 --display=True --learning_rate=0.025 --learning_rate_minimum=0.0025
$ python main.py --network_header_type=mlp --network_output_type=dueling --observation_dims='[16]' --env_name=CorridorSmall-v5 --t_learn_start=0.1 --learning_rate_decay_step=0.1 --history_length=1 --n_action_repeat=1 --t_ep_end=10 --display=True --learning_rate=0.025 --learning_rate_minimum=0.0025
$ python main.py --network_header_type=mlp --network_output_type=dueling --double_q=True --observation_dims='[16]' --env_name=CorridorSmall-v5 --t_learn_start=0.1 --learning_rate_decay_step=0.1 --history_length=1 --n_action_repeat=1 --t_ep_end=10 --display=True --learning_rate=0.025 --learning_rate_minimum=0.0025

Results

Result of Corridor-v5 in [4] for DQN (purple), DDQN (red), Dueling DQN (green), Dueling DDQN (blue).

Result of `Breakout-v0' for DQN without frame-skip (white-blue), DQN with frame-skip (light purple), Dueling DDQN (dark blue).

The hyperparameters and gradient clipping are not implemented as it is as [4].

References

Author

Taehoon Kim / @carpedm20

TensorFlow implementation of Deep Reinforcement Learning papers

Related tags

Overview

Deep Reinforcement Learning in TensorFlow

Requirements

Usage

Results

References

Author

Owner

Taehoon Kim

This repo is developed for Strong Baseline For Vehicle Re-Identification in Track 2 Ai-City-2021 Challenges

GRF: Learning a General Radiance Field for 3D Representation and Rendering

DWIPrep is a robust and easy-to-use pipeline for preprocessing of diverse dMRI data.

Code for the paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks"

Dist2Dec: A Simplicial Neural Network for Homology Localization

Python Library for Signal/Image Data Analysis with Transport Methods

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.

Multimodal Descriptions of Social Concepts: Automatic Modeling and Detection of (Highly Abstract) Social Concepts evoked by Art Images

A machine learning project which can detect and predict the skin disease through image recognition.

MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research

Build tensorflow keras model pipelines in a single line of code. Created by Ram Seshadri. Collaborators welcome. Permission granted upon request.

A curated list of awesome game datasets, and tools to artificial intelligence in games

Code for the AAAI-2022 paper: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

Lightweight, Python library for fast and reproducible experimentation :microscope:

[ICCV 2021] Released code for Causal Attention for Unbiased Visual Recognition

A python library to build Model Trees with Linear Models at the leaves.

Transfer Learning for Pose Estimation of Illustrated Characters

[ICCV-2021] An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation