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74db141d5e
* SAC algorithm * SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces * Soft Actor Critic - code cleanup * code cleanup * V-head initialization fix * SAC benchmarks * SAC Documentation * typo fix * documentation fixes * documentation and version update * README typo
72 lines
3.0 KiB
Python
72 lines
3.0 KiB
Python
from rl_coach.agents.soft_actor_critic_agent import SoftActorCriticAgentParameters
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from rl_coach.architectures.layers import Dense
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from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
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from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
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from rl_coach.filters.filter import InputFilter
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from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter
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from rl_coach.environments.environment import SingleLevelSelection
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from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
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from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
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from rl_coach.graph_managers.graph_manager import ScheduleParameters
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####################
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# Graph Scheduling #
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####################
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# see graph_manager.py for possible schedule parameters
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schedule_params = ScheduleParameters()
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schedule_params.improve_steps = EnvironmentSteps(3000000)
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schedule_params.steps_between_evaluation_periods = EnvironmentSteps(1000)
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schedule_params.evaluation_steps = EnvironmentEpisodes(1)
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schedule_params.heatup_steps = EnvironmentSteps(10000)
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#########
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# Agent #
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#########
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agent_params = SoftActorCriticAgentParameters()
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# override default parameters:
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# value (v) networks parameters
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agent_params.network_wrappers['v'].batch_size = 256
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agent_params.network_wrappers['v'].learning_rate = 0.0003
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agent_params.network_wrappers['v'].middleware_parameters.scheme = [Dense(256)]
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# critic (q) network parameters
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agent_params.network_wrappers['q'].heads_parameters[0].network_layers_sizes = (256, 256)
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agent_params.network_wrappers['q'].batch_size = 256
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agent_params.network_wrappers['q'].learning_rate = 0.0003
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# actor (policy) network parameters
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agent_params.network_wrappers['policy'].batch_size = 256
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agent_params.network_wrappers['policy'].learning_rate = 0.0003
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agent_params.network_wrappers['policy'].middleware_parameters.scheme = [Dense(256)]
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# Input Filter
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# SAC requires reward scaling for Mujoco environments.
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# according to the paper:
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# Hopper, Walker-2d, HalfCheetah, Ant - requires scaling of 5
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# Humanoid - requires scaling of 20
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agent_params.input_filter = InputFilter()
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agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(5))
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###############
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# Environment #
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###############
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env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))
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########
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# Test #
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########
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preset_validation_params = PresetValidationParameters()
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preset_validation_params.test = True
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preset_validation_params.min_reward_threshold = 400
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preset_validation_params.max_episodes_to_achieve_reward = 2200
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preset_validation_params.reward_test_level = 'inverted_pendulum'
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preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper']
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graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
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schedule_params=schedule_params, vis_params=VisualizationParameters(),
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preset_validation_params=preset_validation_params)
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