agent_params.algorithm.policy_gradient_rescaler = PolicyGradientRescaler.GAE
agent_params.network_wrappers['main'].learning_rate = 0.0001
agent_params.input_filter = MujocoInputFilter()
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.
agent_params.exploration = CategoricalParameters()
###############
# Environment #
###############
env_params = DoomEnvironmentParameters()
env_params.level = 'basic'
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 = 20
#########
agent_params = MixedMonteCarloAgentParameters()
agent_params.network_wrappers['main'].learning_rate = 0.00025
agent_params.exploration.epsilon_schedule = LinearSchedule(0.5, 0, 10000)
agent_params.exploration.evaluation_epsilon = 0
agent_params.memory.max_size = (MemoryGranularity.Episodes, 200)
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(
1000)
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1)
agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False
###############
# Environment #
###############
env_params = DoomEnvironmentParameters(level='HEALTH_GATHERING')
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test_using_a_trace_test = False
# disabling this test for now, as it takes too long to converge
# preset_validation_params.test = True
# preset_validation_params.min_reward_threshold = 1000
# preset_validation_params.max_episodes_to_achieve_reward = 300
graph_manager = BasicRLGraphManager(
agent_params=agent_params,
env_params=env_params,
schedule_params=schedule_params,
# the original DFP code decays epsilon in ~1.5M steps. Only that unlike other most other papers, these are 1.5M
# training steps. i.e. it is equivalent to once every 8 playing steps (when a training batch is sampled).
# so this is 1.5M*8 =~ 12M playing steps per worker.
# TODO allow the epsilon schedule to be defined in terms of training steps.
agent_params.exploration.epsilon_schedule = LinearSchedule(1, 0, 12000000)
agent_params.exploration.evaluation_epsilon = 0
agent_params.algorithm.use_accumulated_reward_as_measurement = False
agent_params.algorithm.goal_vector = [0.5, 0.5, 1] # ammo, health, frag count
agent_params.network_wrappers['main'].input_embedders_parameters[
'measurements'].input_rescaling['vector'] = 100.
agent_params.algorithm.scale_measurements_targets['GameVariable.HEALTH'] = 30.0
agent_params.algorithm.scale_measurements_targets['GameVariable.AMMO2'] = 7.5
agent_params.algorithm.scale_measurements_targets['GameVariable.USER2'] = 1.0
agent_params.network_wrappers['main'].learning_rate_decay_rate = 0.3
agent_params.network_wrappers['main'].learning_rate_decay_steps = 250000
agent_params.network_wrappers['main'].input_embedders_parameters[
'measurements'].input_offset['vector'] = 0.5
agent_params.network_wrappers['main'].input_embedders_parameters[
'observation'].input_offset['vector'] = 0.5
###############
# Environment #
###############
env_params = DoomEnvironmentParameters(level='BATTLE_COACH_LOCAL')
env_params.cameras = [DoomEnvironment.CameraTypes.OBSERVATION]
graph_manager = BasicRLGraphManager(agent_params=agent_params,
env_params=env_params,
schedule_params=schedule_params,
vis_params=VisualizationParameters())
agent_params.network_wrappers['main'].input_embedders_parameters[
'observation'].scheme = EmbedderScheme.Medium
agent_params.network_wrappers['main'].input_embedders_parameters[
'measurements'].scheme = EmbedderScheme.Medium
agent_params.network_wrappers['main'].input_embedders_parameters[
'goal'].scheme = EmbedderScheme.Medium
agent_params.network_wrappers[
'main'].middleware_parameters.scheme = MiddlewareScheme.Medium
# scale the target measurements according to the paper (dividing by standard deviation)
agent_params.algorithm.scale_measurements_targets['GameVariable.HEALTH'] = 30.0
###############
# Environment #
###############
env_params = DoomEnvironmentParameters()
env_params.level = 'HEALTH_GATHERING_SUPREME_COACH_LOCAL'
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [
SelectedPhaseOnlyDumpMethod(RunPhase.TEST),
MaxDumpMethod()
]
vis_params.dump_mp4 = False
graph_manager = BasicRLGraphManager(
agent_params=agent_params,
env_params=env_params,
schedule_params=schedule_params,
vis_params=vis_params,
)
