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dqn experiment link

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Varuna Jayasiri
2021-10-02 19:01:32 +05:30
parent 06a79203e5
commit dc13d22873
10 changed files with 13 additions and 13 deletions
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2
labml_nn/rl/dqn/__init__.py
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@ -19,7 +19,7 @@ This is a [PyTorch](https://pytorch.org) implementation of paper
Here is the [experiment](experiment.html) and [model](model.html) implementation.
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb)
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/a0da8048235511ecb9affd797fa27714)
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/fe1ad986237511ec86e8b763a2d3f710)
"""
from typing import Tuple

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labml_nn/rl/dqn/experiment.ipynb
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@ -146,7 +146,7 @@
" # Target model updating interval\n",
" 'update_target_model': 250,\n",
" # Learning rate.\n",
" 'learning_rate': FloatDynamicHyperParam(2.5e-4, (0, 1e-3)),\n",
" 'learning_rate': FloatDynamicHyperParam(1e-4, (0, 1e-3)),\n",
"}"
]
},

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labml_nn/rl/dqn/experiment.py
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@ -10,7 +10,7 @@ This experiment trains a Deep Q Network (DQN) to play Atari Breakout game on Ope
It runs the [game environments on multiple processes](../game.html) to sample efficiently.
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb)
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/a0da8048235511ecb9affd797fa27714)
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/fe1ad986237511ec86e8b763a2d3f710)
"""
import numpy as np

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labml_nn/rl/dqn/model.py
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@ -7,7 +7,7 @@ summary: Implementation of neural network model for Deep Q Network (DQN).
# Deep Q Network (DQN) Model
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb)
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/a0da8048235511ecb9affd797fa27714)
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/fe1ad986237511ec86e8b763a2d3f710)
"""
import torch

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labml_nn/rl/dqn/replay_buffer.py
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@ -10,7 +10,7 @@ This implements paper [Prioritized experience replay](https://papers.labml.ai/pa
using a binary segment tree.
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb)
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/a0da8048235511ecb9affd797fa27714)
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/fe1ad986237511ec86e8b763a2d3f710)
"""
import random