From f483ccd8ef841e6b074b6d2822293a867e8f2e35 Mon Sep 17 00:00:00 2001
From: Erwin Coumans <erwincoumans@google.com>
Date: Thu, 16 Nov 2017 16:49:25 +0000
Subject: [PATCH] remote org configs

---
 .../gym/pybullet_envs/agents/configs2.py      | 130 ------------------
 1 file changed, 130 deletions(-)
 delete mode 100644 examples/pybullet/gym/pybullet_envs/agents/configs2.py

diff --git a/examples/pybullet/gym/pybullet_envs/agents/configs2.py b/examples/pybullet/gym/pybullet_envs/agents/configs2.py
deleted file mode 100644
index 27bd70d1f..000000000
--- a/examples/pybullet/gym/pybullet_envs/agents/configs2.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# Copyright 2017 The TensorFlow Agents Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Example configurations using the PPO algorithm."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-# pylint: disable=unused-variable
-
-import tensorflow as tf
-
-from . import ppo
-from . import networks
-
-
-def default():
-  """Default configuration for PPO."""
-  # General
-  algorithm = ppo.PPOAlgorithm
-  num_agents = 30
-  eval_episodes = 30
-  use_gpu = False
-  # Network
-  network = networks.feed_forward_gaussian
-  weight_summaries = dict(
-      all=r'.*', policy=r'.*/policy/.*', value=r'.*/value/.*')
-  policy_layers = 200, 100
-  value_layers = 200, 100
-  init_mean_factor = 0.1
-  init_logstd = -1
-  # Optimization
-  update_every = 30
-  update_epochs = 25
-  optimizer = tf.train.AdamOptimizer
-  learning_rate = 1e-4
-  # Losses
-  discount = 0.995
-  kl_target = 1e-2
-  kl_cutoff_factor = 2
-  kl_cutoff_coef = 1000
-  kl_init_penalty = 1
-  return locals()
-
-
-def pendulum():
-  """Configuration for the pendulum classic control task."""
-  locals().update(default())
-  # Environment
-  env = 'Pendulum-v0'
-  max_length = 200
-  steps = 2e6  # 2M
-  return locals()
-
-
-def reacher():
-  """Configuration for MuJoCo's reacher task."""
-  locals().update(default())
-  # Environment
-  env = 'Reacher-v1'
-  max_length = 1000
-  steps = 5e6  # 5M
-  discount = 0.985
-  update_every = 60
-  return locals()
-
-
-def cheetah():
-  """Configuration for MuJoCo's half cheetah task."""
-  locals().update(default())
-  # Environment
-  env = 'HalfCheetah-v1'
-  max_length = 1000
-  steps = 1e7  # 10M
-  discount = 0.99
-  return locals()
-
-
-def walker():
-  """Configuration for MuJoCo's walker task."""
-  locals().update(default())
-  # Environment
-  env = 'Walker2d-v1'
-  max_length = 1000
-  steps = 1e7  # 10M
-  return locals()
-
-
-def hopper():
-  """Configuration for MuJoCo's hopper task."""
-  locals().update(default())
-  # Environment
-  env = 'Hopper-v1'
-  max_length = 1000
-  steps = 1e7  # 10M
-  update_every = 60
-  return locals()
-
-
-def ant():
-  """Configuration for MuJoCo's ant task."""
-  locals().update(default())
-  # Environment
-  env = 'Ant-v1'
-  max_length = 1000
-  steps = 2e7  # 20M
-  return locals()
-
-
-def humanoid():
-  """Configuration for MuJoCo's humanoid task."""
-  locals().update(default())
-  # Environment
-  env = 'Humanoid-v1'
-  max_length = 1000
-  steps = 5e7  # 50M
-  update_every = 60
-  return locals()