129 lines
2.9 KiB
Python
129 lines
2.9 KiB
Python
# Copyright 2017 The TensorFlow Agents Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Example configurations using the PPO algorithm."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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# pylint: disable=unused-variable
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from pybullet_envs.minitaur.agents import ppo
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from pybullet_envs.minitaur.agents.scripts import networks
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def default():
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"""Default configuration for PPO."""
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# General
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algorithm = ppo.PPOAlgorithm
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num_agents = 10
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eval_episodes = 25
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use_gpu = False
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# Network
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network = networks.ForwardGaussianPolicy
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weight_summaries = dict(
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all=r'.*',
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policy=r'.*/policy/.*',
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value=r'.*/value/.*')
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policy_layers = 200, 100
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value_layers = 200, 100
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init_mean_factor = 0.05
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init_logstd = -1
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# Optimization
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update_every = 25
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policy_optimizer = 'AdamOptimizer'
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value_optimizer = 'AdamOptimizer'
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update_epochs_policy = 50
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update_epochs_value = 50
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policy_lr = 1e-4
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value_lr = 3e-4
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# Losses
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discount = 0.985
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kl_target = 1e-2
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kl_cutoff_factor = 2
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kl_cutoff_coef = 1000
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kl_init_penalty = 1
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return locals()
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def pendulum():
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"""Configuration for the pendulum classic control task."""
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locals().update(default())
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# Environment
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env = 'Pendulum-v0'
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max_length = 200
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steps = 1e6 # 1M
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return locals()
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def cheetah():
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"""Configuration for MuJoCo's half cheetah task."""
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locals().update(default())
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# Environment
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env = 'HalfCheetah-v1'
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max_length = 1000
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steps = 1e7 # 10M
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return locals()
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def walker():
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"""Configuration for MuJoCo's walker task."""
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locals().update(default())
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# Environment
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env = 'Walker2d-v1'
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max_length = 1000
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steps = 1e7 # 10M
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return locals()
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def reacher():
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"""Configuration for MuJoCo's reacher task."""
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locals().update(default())
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# Environment
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env = 'Reacher-v1'
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max_length = 1000
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steps = 1e7 # 10M
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return locals()
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def hopper():
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"""Configuration for MuJoCo's hopper task."""
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locals().update(default())
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# Environment
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env = 'Hopper-v1'
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max_length = 1000
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steps = 2e7 # 20M
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return locals()
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def ant():
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"""Configuration for MuJoCo's ant task."""
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locals().update(default())
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# Environment
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env = 'Ant-v1'
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max_length = 1000
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steps = 5e7 # 50M
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return locals()
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def humanoid():
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"""Configuration for MuJoCo's humanoid task."""
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locals().update(default())
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# Environment
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env = 'Humanoid-v1'
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max_length = 1000
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steps = 5e7 # 50M
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return locals()
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