gryf/coach
1
0
Fork 0
mirror of https://github.com/gryf/coach.git synced 2026-03-31 00:53:32 +02:00
Code Issues Projects Releases Wiki Activity

network_imporvements branch merge

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

23
rl_coach/presets/Pendulum_HAC.py
View File

@@ -1,13 +1,11 @@
import numpy as np
from rl_coach.agents.hac_ddpg_agent import HACDDPGAgentParameters
from rl_coach.architectures.tensorflow_components.architecture import Dense
from rl_coach.base_parameters import VisualizationParameters, EmbeddingMergerType, EmbedderScheme
from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, RunPhase, TrainingSteps
from rl_coach.environments.environment import SelectedPhaseOnlyDumpMethod
from rl_coach.environments.gym_environment import Mujoco
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.base_parameters import VisualizationParameters, EmbeddingMergerType, EmbedderScheme
from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps
from rl_coach.environments.gym_environment import GymVectorEnvironment
from rl_coach.exploration_policies.e_greedy import EGreedyParameters
from rl_coach.exploration_policies.ou_process import OUProcessParameters
from rl_coach.graph_managers.graph_manager import ScheduleParameters
@@ -66,7 +64,7 @@ top_agent_params.exploration.theta = 0.1
top_actor = top_agent_params.network_wrappers['actor']
top_actor.input_embedders_parameters = {'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty),
'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty)}
top_actor.middleware_parameters.scheme = [Dense([64])] * 3
top_actor.middleware_parameters.scheme = [Dense(64)] * 3
top_actor.learning_rate = 0.001
top_actor.batch_size = 4096
@@ -76,7 +74,7 @@ top_critic.input_embedders_parameters = {'observation': InputEmbedderParameters(
'action': InputEmbedderParameters(scheme=EmbedderScheme.Empty),
'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty)}
top_critic.embedding_merger_type = EmbeddingMergerType.Concat
top_critic.middleware_parameters.scheme = [Dense([64])] * 3
top_critic.middleware_parameters.scheme = [Dense(64)] * 3
top_critic.learning_rate = 0.001
top_critic.batch_size = 4096
@@ -107,7 +105,7 @@ bottom_agent_params.exploration.continuous_exploration_policy_parameters.theta =
bottom_actor = bottom_agent_params.network_wrappers['actor']
bottom_actor.input_embedders_parameters = {'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty),
'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty)}
bottom_actor.middleware_parameters.scheme = [Dense([64])] * 3
bottom_actor.middleware_parameters.scheme = [Dense(64)] * 3
bottom_actor.learning_rate = 0.001
bottom_actor.batch_size = 4096
@@ -117,7 +115,7 @@ bottom_critic.input_embedders_parameters = {'observation': InputEmbedderParamete
'action': InputEmbedderParameters(scheme=EmbedderScheme.Empty),
'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty)}
bottom_critic.embedding_merger_type = EmbeddingMergerType.Concat
bottom_critic.middleware_parameters.scheme = [Dense([64])] * 3
bottom_critic.middleware_parameters.scheme = [Dense(64)] * 3
bottom_critic.learning_rate = 0.001
bottom_critic.batch_size = 4096
@@ -128,8 +126,7 @@ agents_params = [top_agent_params, bottom_agent_params]
###############
time_limit = 1000
env_params = Mujoco()
env_params.level = "rl_coach.environments.mujoco.pendulum_with_goals:PendulumWithGoals"
env_params = GymVectorEnvironment(level="rl_coach.environments.mujoco.pendulum_with_goals:PendulumWithGoals")
env_params.additional_simulator_parameters = {"time_limit": time_limit,
"random_goals_instead_of_standing_goal": False,
"polar_coordinates": polar_coordinates,
@@ -138,8 +135,6 @@ env_params.frame_skip = 10
env_params.custom_reward_threshold = -time_limit + 1
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST)]
vis_params.dump_mp4 = False
vis_params.native_rendering = False
graph_manager = HACGraphManager(agents_params=agents_params, env_params=env_params,