karpathy
/ pg-pong.py
Created
May 30, 2016 22:50
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
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| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
shanest
/ cartpole_pg.py
Last active
February 17, 2020 03:26
Policy gradients for reinforcement learning in TensorFlow (OpenAI gym CartPole environment)
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| #!/usr/bin/env python | |
| import gym | |
| import numpy as np | |
| import tensorflow as tf | |
| class PolicyGradientAgent(object): | |
| def __init__(self, hparams, sess): |