Study Log (2021.01)

2021-01-31

• S-K RL
  • train_FT10_ppo_node_only.py
    • do_simulate_on_aggregated_state()
    • value_loss, action_loss, dist_entropy = agent.fit(eval=0, reward_setting=’utilization’, device=device, return_scaled=False)
    • eval_performance = evaluate_agent_on_aggregated_state(simulator=sim, agent=agent, device=’cpu’, mode=’node_mode’)
    • val_performance = validation(agent, path, mode=’node_mode’)
  • SBJSSP_report_results.ipynb
    • def get_swapping_ops(blocking_op, machine_dict)
    • class blMachine(Machine)
    • class blMachineManager(MachineManager)
    • class blSimulator(Simulator)
    • def evaluate_agent_on_aggregated_state(simulator, agent, device, mode=’edge_mode’)
    • def evaluate_agent_on_aggregated_state_DR(simulator, mode=’MTWR’)
    • def SBJSSP_validation(agent, path, device=’cpu’, optimums=None, num_val=100, new_attr=False, mode=’edge_mode’, special=None, DR=None)
    • def compare_with_optimum(makespans, files, plot=False, scheduler_name=None)
    • def evaluate_agent_on_aggregated_state_DR_interrupted(simulator, mode=’MTWR’, shutdown_prob=0.2)
    • def evaluate_agent_on_aggregated_state_interrupted(simulator, agent, device, mode=’edge_mode’, shutdown_prob=0.2)
    • def SBJSSP_validation_interrupted(agent, path, device=’cpu’, optimums=None, num_val=100, new_attr=False, mode=’edge_mode’, special=None, DR=None, shutdown_prob=0.2)
    • def random_simulator(min_m=5, max_m=10, max_job=10, new_attr=False, special=’SBJSSP’):
    • def do_simulate_on_aggregated_state_interrupted(simulator, agent, episode_index, device, reward=’utilization’, scaled=False, mode=’edge_mode’,shutdown_prob=0.2)
• 팡요랩
  • 강화학습 3강 - Planning by Dynamic Programming

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2021-01-30

• S-K RL
  • train_FT10_ppo_node_only.py
    • pyjssp > benchmarks.py
    • pyjssp > simulators.py
    • pyjssp > jobShopSamplers.py
    • pyjssp > operationHelpers.py
    • src > node_only_agents.py
    • src/agents.py
    • src/rl_networks.py
    • torch > linear.py
    • src/training_utils.py
    • src/utils.py
    • src > nn.py
• 팡요랩
  • 강화학습 2강 - Markov Decision Process

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2021-01-29

• S-K RL
  • train_FT10_ppo_node_only.py
    • pyjssp > benchmarks.py
    • pyjssp > simulators.py
    • pyjssp > jobShopSamplers.py
    • pyjssp > operationHelpers.py
    • src > node_only_agents.py
    • src/agents.py
    • src/rl_networks.py
    • torch > linear.py
    • src/training_utils.py
    • src/utils.py
    • src > nn.py
• 팡요랩
  • 강화학습 1강 - 강화학습 introduction

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2021-01-01

• S-K RL
  • train_FT10_ppo_node_only.py
    • pyjssp > benchmarks.py
    • pyjssp > simulators.py
    • pyjssp > jobShopSamplers.py
    • pyjssp > operationHelpers.py
    • src > node_only_agents.py
    • src/agents.py
    • src/rl_networks.py
    • torch > linear.py
    • src/training_utils.py
    • src/utils.py
    • src > nn.py

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Template

• Fundamental of Reinforcement Learning
  • Chapter #.
• 모두를 위한 머신러닝/딥러닝 강의
  • Lecture #.
• UCL Course on RL
  • Lecture #.
• Reinforcement Learning
  • Page #.
• 팡요랩
  • 강화학습 1강 - 강화학습 introduction
  • 강화학습 2강 - Markov Decision Process
  • 강화학습 3강 - Planning by Dynamic Programming
  • 강화학습 4강 - Model Free Prediction
  • 강화학습 5강 - Model Free Control
  • 강화학습 6강 - Value Function Approximation
  • 강화학습 7강 - Policy Gradient
  • 강화학습 8강 - Integrating Learning and Planning
  • 강화학습 9강 - Exploration and Exploitation
  • 강화학습 10강 - Classic Games
• Pattern Recognition & Machine Learning
• S-K RL
• multi_step_actor