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network_imporvements branch merge

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This commit is contained in:
Shadi Endrawis
2018-10-02 13:41:46 +03:00
parent 72ea933384
commit 51726a5b80
110 changed files with 1639 additions and 1161 deletions
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33
rl_coach/presets/Fetch_DDPG_HER_baselines.py
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@@ -1,12 +1,13 @@
from rl_coach.agents.ddpg_agent import DDPGAgentParameters
from rl_coach.architectures.tensorflow_components.architecture import Dense
from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, PresetValidationParameters
from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps, RunPhase
from rl_coach.environments.environment import SelectedPhaseOnlyDumpMethod, MaxDumpMethod, SingleLevelSelection
from rl_coach.environments.gym_environment import Mujoco, MujocoInputFilter, fetch_v1
from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps
from rl_coach.environments.environment import SingleLevelSelection
from rl_coach.environments.gym_environment import GymVectorEnvironment, fetch_v1
from rl_coach.exploration_policies.e_greedy import EGreedyParameters
from rl_coach.filters.filter import InputFilter
from rl_coach.filters.observation.observation_clipping_filter import ObservationClippingFilter
from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
@@ -44,7 +45,7 @@ actor_network.input_embedders_parameters = {
'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty),
'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty)
}
actor_network.middleware_parameters = FCMiddlewareParameters(scheme=[Dense([256]), Dense([256]), Dense([256])])
actor_network.middleware_parameters = FCMiddlewareParameters(scheme=[Dense(256), Dense(256), Dense(256)])
actor_network.heads_parameters[0].batchnorm = False
# critic
@@ -59,7 +60,7 @@ critic_network.input_embedders_parameters = {
'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty),
'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty)
}
critic_network.middleware_parameters = FCMiddlewareParameters(scheme=[Dense([256]), Dense([256]), Dense([256])])
critic_network.middleware_parameters = FCMiddlewareParameters(scheme=[Dense(256), Dense(256), Dense(256)])
agent_params.algorithm.discount = 0.98
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(1)
@@ -90,10 +91,10 @@ agent_params.exploration.evaluation_epsilon = 0
agent_params.exploration.continuous_exploration_policy_parameters.noise_percentage_schedule = ConstantSchedule(0.1)
agent_params.exploration.continuous_exploration_policy_parameters.evaluation_noise_percentage = 0
agent_params.input_filter = MujocoInputFilter()
agent_params.input_filter = InputFilter()
agent_params.input_filter.add_observation_filter('observation', 'clipping', ObservationClippingFilter(-200, 200))
agent_params.pre_network_filter = MujocoInputFilter()
agent_params.pre_network_filter = InputFilter()
agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation',
ObservationNormalizationFilter(name='normalize_observation'))
agent_params.pre_network_filter.add_observation_filter('achieved_goal', 'normalize_achieved_goal',
@@ -104,27 +105,17 @@ agent_params.pre_network_filter.add_observation_filter('desired_goal', 'normaliz
###############
# Environment #
###############
env_params = Mujoco()
env_params.level = SingleLevelSelection(fetch_v1)
env_params = GymVectorEnvironment(level=SingleLevelSelection(fetch_v1))
env_params.custom_reward_threshold = -49
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
# preset_validation_params.test = True
# preset_validation_params.min_reward_threshold = 200
# preset_validation_params.max_episodes_to_achieve_reward = 600
# preset_validation_params.reward_test_level = 'inverted_pendulum'
preset_validation_params.trace_test_levels = ['slide', 'pick_and_place', 'push', 'reach']
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=vis_params,
schedule_params=schedule_params, vis_params=VisualizationParameters(),
preset_validation_params=preset_validation_params)