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

A semantic segmentation toolbox based on PyTorch

Boundary-preserving Mask R-CNN (ECCV 2020)

ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing

CROSS-LINGUAL ABILITY OF MULTILINGUAL BERT: AN EMPIRICAL STUDY

OSLO: Open Source framework for Large-scale transformer Optimization

Nested Graph Neural Network (NGNN) is a general framework to improve a base GNN's expressive power and performance

Combining Diverse Feature Priors

Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.

Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.

Iran Open Source Hackathon

Self-Supervised Pre-Training for Transformer-Based Person Re-Identification

Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions

DeepCAD: A Deep Generative Network for Computer-Aided Design Models

A particular navigation route using satellite feed and can help in toll operations & traffic managemen

COVID-Net Open Source Initiative

Matlab Python Heuristic Battery Opt - SMOP conversion and manual conversion

RETRO-pytorch - Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch

Code of paper: "DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks"

TensorFlow implementation of Adaptive Information Transfer Multi-task (AITM) framework. Code for the paper submitted to KDD21: Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning for Customer Acquisition.

This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos