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coach/rl_coach/presets/Doom_Basic_A3C.py
Gal Novik c9738280fd Require Python 3.6 + Changes to CI configuration (#452)
* Change build_*_env jobs to pull base image of current "tag"
  instead of "master" image
* Change nightly flow so build_*_env jobs now gated by build_base (so
  change in previous bullet works in nightly)
* Bugfix in CheckpointDataStore: Call to object.__init__ with
  parameters
* Disabling unstable Doom A3C and ACER golden tests
2020-07-26 16:11:22 +03:00

54 lines
2.2 KiB
Python

from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters
from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
from rl_coach.environments.doom_environment import DoomEnvironmentParameters
from rl_coach.filters.filter import InputFilter
from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
from rl_coach.graph_managers.graph_manager import ScheduleParameters
####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(10000000000)
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10)
schedule_params.evaluation_steps = EnvironmentEpisodes(1)
schedule_params.heatup_steps = EnvironmentSteps(0)
#########
# Agent #
#########
agent_params = ActorCriticAgentParameters()
agent_params.algorithm.policy_gradient_rescaler = PolicyGradientRescaler.GAE
agent_params.network_wrappers['main'].learning_rate = 0.0001
agent_params.input_filter = InputFilter()
agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(1/100.))
agent_params.algorithm.num_steps_between_gradient_updates = 30
agent_params.algorithm.apply_gradients_every_x_episodes = 1
agent_params.algorithm.gae_lambda = 1.0
agent_params.algorithm.beta_entropy = 0.01
agent_params.network_wrappers['main'].clip_gradients = 40.
###############
# Environment #
###############
env_params = DoomEnvironmentParameters(level='basic')
########
# Test #
########
preset_validation_params = PresetValidationParameters()
# preset_validation_params.test = True
# preset_validation_params.min_reward_threshold = 20
# preset_validation_params.max_episodes_to_achieve_reward = 400
# preset_validation_params.num_workers = 8
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=VisualizationParameters(),
preset_validation_params=preset_validation_params)