from rl_coach.agents.bootstrapped_dqn_agent import BootstrappedDQNAgentParameters
from rl_coach.base_parameters import VisualizationParameters
from rl_coach.environments.gym_environment import Mujoco
from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
from rl_coach.graph_managers.graph_manager import ScheduleParameters
from rl_coach.memories.memory import MemoryGranularity
from rl_coach.schedules import ConstantSchedule

from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
from rl_coach.exploration_policies.ucb import UCBParameters

N = 20
num_output_head_copies = 20

####################
# Graph Scheduling #
####################

schedule_params = ScheduleParameters()
schedule_params.improve_steps = EnvironmentEpisodes(2000)
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10)
schedule_params.evaluation_steps = EnvironmentEpisodes(1)
schedule_params.heatup_steps = EnvironmentSteps(N)

####################
# DQN Agent Params #
####################
agent_params = BootstrappedDQNAgentParameters()
agent_params.network_wrappers['main'].learning_rate = 0.00025
agent_params.memory.max_size = (MemoryGranularity.Transitions, 1000000)
agent_params.algorithm.discount = 0.99
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(4)
agent_params.network_wrappers['main'].num_output_head_copies = num_output_head_copies
agent_params.network_wrappers['main'].rescale_gradient_from_head_by_factor = [1.0/num_output_head_copies]*num_output_head_copies
agent_params.exploration = UCBParameters()
agent_params.exploration.bootstrapped_data_sharing_probability = 1.0
agent_params.exploration.architecture_num_q_heads = num_output_head_copies
agent_params.exploration.epsilon_schedule = ConstantSchedule(0)
agent_params.exploration.lamb = 10
agent_params.input_filter = NoInputFilter()
agent_params.output_filter = NoOutputFilter()

###############
# Environment #
###############
env_params = Mujoco()
env_params.level = 'rl_coach.environments.toy_problems.exploration_chain:ExplorationChain'

env_params.additional_simulator_parameters = {'chain_length': N, 'max_steps': N+7}

vis_params = VisualizationParameters()

graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
                                    schedule_params=schedule_params, vis_params=vis_params)