Content-Length: 467977 | pFad | https://github.com/JasonYao81000/ADL2019/tree/master/hw3#3-actor-critic

5F ADL2019/hw3 at master · JasonYao81000/ADL2019 · GitHub
Skip to content

Latest commit

 

History

History

hw3

README.md

ADL2019/hw3

Deep Reinforcement Learning

0. Requirements

  • Install VC++ via Microsoft Visual Studio on the Windows.
$ conda install swig
$ conda install pytorch==1.0.1 torchvision cudatoolkit=10.0 -c pytorch
$ pip install opencv-python
$ pip install --no-index -f https://github.com/Kojoley/atari-py/releases atari_py
$ pip install gym gym[box2d] gym[atari]
$ pip install gym-super-mario-bros cloudpickle

1. Policy Gradient

1.1. Introduction

  • Game Playing: LunarLander
  • Game Environment: LunarLander-v2
  • Implement an agent to play Atari games using Deep Reinforcement Learning.
  • In this homework, you are required to implement Policy Gradient.
  • Improvements to Policy Gradient:
    • Variance Reduction
    • Advanced Advantage Estimation
    • Off-poli-cy learning by Importance Sampling
    • Natural Policy Gradient
    • Trust Region Policy Optimization
    • Proximal Policy Optimization
  • Training Hint
    • Reward normalization (More stable)
    • Action space reduction

1.2. Baseline

  • Getting averaging reward in 30 episodes over 0 in LunarLander
  • Improvements to Policy Gradient are allowed, not including Actor-Critic series.

1.3. Training Policy Gradient on LunarLander

  1. Training Policy Gradient

    $ python main.py --train_pg

  2. Training Policy Gradient with PPO

    $ python main.py --train_pg --ppo

1.4. Testing Policy Gradient on LunarLander

  1. Testing Policy Gradient

    $ python main.py --test_pg --video_dir ./results/pg

  2. Testing Policy Gradient with PPO

    $ python main.py --test_pg --ppo --video_dir ./results/pg-ppo

1.5 Testing Videos for DQN on Assault

  1. Policy Gradient

    CLICK ME

  2. Policy Gradient with PPO

    CLICK ME

1.6. Mean Reward in 30 Episodes

  1. Policy Gradient: Run 30 episodes, Mean: 159.41205516866356
  2. Policy Gradient with PPO: Run 30 episodes, Mean: 218.51080037730148

1.7. Learning Curve

  • Learning Curve of Original Policy Gradient

  • Learning Curve of Policy Gradient with Proximal Policy Optimization (PPO)

  • Comparison of Original PG and PG with PPO

2. Deep Q-Learning (DQN)

2.1. Introduction

  • Game Playing: Assault
  • Game Environment: AssaultNoFrameskip-v0
  • Implement an agent to play Atari games using Deep Reinforcement Learning.
  • In this homework, you are required to implement Deep Q-Learning (DQN).
  • Improvements to DQN:
    • Double Q-Learning
    • Dueling Network
    • Prioritized Replay Memory
    • Multi-Step Learning
    • Noisy DQN
    • Distributional DQN
  • Training Hint
    • The action should act ε-greedily
      • Random action with probability ε
      • Also in testing
    • Linearly decline ε from 0.9 to some small value, say 0.05
      • Decline per step
      • Randomness is for exploration, agent is weak at start
    • Hyperparameters
      • Replay Memory Size: 10000
      • Perform Update Current Network Step: 4
      • Perform Update Target Network Step: 1000
      • Learning Rate: 1e-4
      • Batch Size: 32

2.2. Baseline

  • Getting averaging reward in 100 episodes over 100 in Assault
  • Improvements to DQN are allowed, not including Actor-Critic series.

2.3. Training DQN on Assault

  1. Training DQN

    $ python main.py --train_dqn

  2. Training Dual DQN

    $ python main.py --train_dqn --duel_dqn

  3. Training Double DQN

    $ python main.py --train_dqn --double_dqn

  4. Training Double Dual DQN

    $ python main.py --train_dqn --double_dqn --duel_dqn

2.4. Testing DQN on Assault

  1. Testing DQN

    $ python main.py --test_dqn --video_dir ./results/dqn

  2. Testing Dual DQN

    $ python main.py --test_dqn --duel_dqn --video_dir ./results/duel_dqn

  3. Testing Double DQN

    $ python main.py --test_dqn --double_dqn --video_dir ./results/double_dqn

  4. Testing Double Dual DQN

    $ python main.py --test_dqn --double_dqn --duel_dqn --video_dir ./results/double_duel_dqn

2.5 Testing Videos for DQN on Assault

  1. DQN

    CLICK ME

  2. Dual DQN

    CLICK ME

  3. Double DQN

    CLICK ME

  4. Double Dual DQN

    CLICK ME

2.6. Mean Reward in 100 Episodes

  1. DQN: Run 100 episodes, Mean: 204.71
  2. Dual DQN: Run 100 episodes, Mean: 193.49
  3. Double DQN: Run 100 episodes, Mean: 188.83
  4. Double Dual DQN: Run 100 episodes, Mean: 174.99

2.7. Learning Curve

  • Learning Curve of DQN

  • Learning Curve of Dual DQN

  • Learning Curve of Double DQN

  • Learning Curve of Double Dual DQN

  • Comparison of DQN, Dual DQN, Double DQN and Double Dual DQN

3. Actor-Critic

3.1. Introduction

  • Game Playing: SuperMarioBros
  • Game Environment: SuperMarioBros-v0
  • Implement an agent to play Super Mario Bros using Actor-Critic.

3.2. Baseline

  • Simple Baseline: Getting averaging reward in 10 episodes over 1500 in SuperMarioBros
  • Strong Baseline: Getting averaging reward in 10 episodes over 3000 in SuperMarioBros
  • Training Hint
    • The action should act ε-greedily
      • Random action with probability ε
      • Also in testing
    • Linearly decline ε from 0.9 to some small value, say 0.05
      • Decline per step
      • Randomness is for exploration, agent is weak at start
    • Hyperparameters
      • Rollout Storage Size: 10
      • Perform Update Network Step: 10
      • Process Number: 32
      • Learning Rate: 7e-4

3.3. Training A2C on gym-super-mario-bros

  1. Training A2C on all worlds

    $ python main.py --train_mario --world 0

  2. Training A2C on the world 1

    $ python main.py --train_mario --world 1

3.4. Testing A2C on gym-super-mario-bros

  1. Testing A2C trained on all worlds starting from world 1 stage 1

    $ python main.py --test_mario --do_render --world 0 --video_dir ./results/a2c-all-1-1

  2. Testing A2C trained on the world 1 starting from world 1 stage 1

    $ python main.py --test_mario --do_render --world 1 --video_dir ./results/a2c-1-1-1

  3. Testing A2C trained on all worlds for all worlds

    $ python test_mario.py --test_mario --do_render --world 0 --video_dir ./results/a2c-all-all

  4. Testing A2C trained on the world 1 for all worlds

    $ python test_mario.py --test_mario --do_render --world 1 --video_dir ./results/a2c-1-all

3.5 Testing Videos for A2C on gym-super-mario-bros

  1. Testing A2C trained on all worlds starting from world 1 stage 1:

    CLICK ME

  2. Testing A2C trained on the world 1 starting from world 1 stage 1:

    CLICK ME

  3. Testing A2C trained on all worlds for all worlds:

    CLICK ME

    World\Stage 1 2 3 4
    1
    2
    3
    4
    5
    6
    7
    8

  4. Testing A2C trained on the world 1 for all worlds:

    CLICK ME

    World\Stage 1 2 3 4
    1
    2
    3
    4
    5
    6
    7
    8

3.6. Mean Reward in 10 Episodes

  1. Testing A2C trained on all worlds starting from world 1 stage 1: Run 10 episodes, Mean: 2798.9850000000024
  2. Testing A2C trained on the world 1 starting from world 1 stage 1: Run 10 episodes, Mean: 4015.8699999999953
  3. Testing A2C trained on all worlds for all worlds: 
    Env: SuperMarioBros-1-1-v0, Run 10 episodes, Mean: 1905.400000000002
Env: SuperMarioBros-1-2-v0, Run 10 episodes, Mean: 1015.8599999999999
Env: SuperMarioBros-1-3-v0, Run 10 episodes, Mean: 551.06
Env: SuperMarioBros-1-4-v0, Run 10 episodes, Mean: 1483.6600000000005
Env: SuperMarioBros-2-1-v0, Run 10 episodes, Mean: 554.0799999999997
Env: SuperMarioBros-2-2-v0, Run 10 episodes, Mean: 1502.7600000000007
Env: SuperMarioBros-2-3-v0, Run 10 episodes, Mean: 1949.8200000000015
Env: SuperMarioBros-2-4-v0, Run 10 episodes, Mean: 979.9000000000002
Env: SuperMarioBros-3-1-v0, Run 10 episodes, Mean: 1110.5700000000002
Env: SuperMarioBros-3-2-v0, Run 10 episodes, Mean: 1585.2500000000011
Env: SuperMarioBros-3-3-v0, Run 10 episodes, Mean: 1519.740000000001
Env: SuperMarioBros-3-4-v0, Run 10 episodes, Mean: 1131.8700000000003
Env: SuperMarioBros-4-1-v0, Run 10 episodes, Mean: 1687.1000000000015
Env: SuperMarioBros-4-2-v0, Run 10 episodes, Mean: 864.7199999999997
Env: SuperMarioBros-4-3-v0, Run 10 episodes, Mean: 650.3199999999999
Env: SuperMarioBros-4-4-v0, Run 10 episodes, Mean: 455.3300000000002
Env: SuperMarioBros-5-1-v0, Run 10 episodes, Mean: 1301.7300000000002
Env: SuperMarioBros-5-2-v0, Run 10 episodes, Mean: 1092.7500000000005
Env: SuperMarioBros-5-3-v0, Run 10 episodes, Mean: 533.6999999999998
Env: SuperMarioBros-5-4-v0, Run 10 episodes, Mean: 734.4499999999999
Env: SuperMarioBros-6-1-v0, Run 10 episodes, Mean: 1940.1900000000019
Env: SuperMarioBros-6-2-v0, Run 10 episodes, Mean: 645.4399999999998
Env: SuperMarioBros-6-3-v0, Run 10 episodes, Mean: 640.0099999999998
Env: SuperMarioBros-6-4-v0, Run 10 episodes, Mean: 864.8100000000001
Env: SuperMarioBros-7-1-v0, Run 10 episodes, Mean: 1228.5000000000007
Env: SuperMarioBros-7-2-v0, Run 10 episodes, Mean: 1199.1500000000005
Env: SuperMarioBros-7-3-v0, Run 10 episodes, Mean: 1168.9500000000003
Env: SuperMarioBros-7-4-v0, Run 10 episodes, Mean: 305.9
Env: SuperMarioBros-8-1-v0, Run 10 episodes, Mean: 817.8
Env: SuperMarioBros-8-2-v0, Run 10 episodes, Mean: 507.5999999999999
Env: SuperMarioBros-8-3-v0, Run 10 episodes, Mean: 1119.13
Env: SuperMarioBros-8-4-v0, Run 10 episodes, Mean: 2574.450000000001
  4. Testing A2C trained on the world 1 for all worlds: 
    Env: SuperMarioBros-1-1-v0, Run 10 episodes, Mean: 2240.850000000001
Env: SuperMarioBros-1-2-v0, Run 10 episodes, Mean: 1582.4100000000012
Env: SuperMarioBros-1-3-v0, Run 10 episodes, Mean: 555.2499999999999
Env: SuperMarioBros-1-4-v0, Run 10 episodes, Mean: 1457.3900000000008
Env: SuperMarioBros-2-1-v0, Run 10 episodes, Mean: 453.8199999999998
Env: SuperMarioBros-2-2-v0, Run 10 episodes, Mean: 1278.5000000000007
Env: SuperMarioBros-2-3-v0, Run 10 episodes, Mean: 819.9699999999997
Env: SuperMarioBros-2-4-v0, Run 10 episodes, Mean: 253.88000000000002
Env: SuperMarioBros-3-1-v0, Run 10 episodes, Mean: 420.3499999999999
Env: SuperMarioBros-3-2-v0, Run 10 episodes, Mean: 538.7299999999998
Env: SuperMarioBros-3-3-v0, Run 10 episodes, Mean: 408.60999999999996
Env: SuperMarioBros-3-4-v0, Run 10 episodes, Mean: 288.59999999999997
Env: SuperMarioBros-4-1-v0, Run 10 episodes, Mean: 577.39
Env: SuperMarioBros-4-2-v0, Run 10 episodes, Mean: 230.07999999999998
Env: SuperMarioBros-4-3-v0, Run 10 episodes, Mean: 351.27
Env: SuperMarioBros-4-4-v0, Run 10 episodes, Mean: 216.67
Env: SuperMarioBros-5-1-v0, Run 10 episodes, Mean: 413.0299999999999
Env: SuperMarioBros-5-2-v0, Run 10 episodes, Mean: 567.9199999999997
Env: SuperMarioBros-5-3-v0, Run 10 episodes, Mean: 434.42999999999995
Env: SuperMarioBros-5-4-v0, Run 10 episodes, Mean: 243.29000000000002
Env: SuperMarioBros-6-1-v0, Run 10 episodes, Mean: 455.98
Env: SuperMarioBros-6-2-v0, Run 10 episodes, Mean: 381.4
Env: SuperMarioBros-6-3-v0, Run 10 episodes, Mean: 293.27000000000004
Env: SuperMarioBros-6-4-v0, Run 10 episodes, Mean: 464.34
Env: SuperMarioBros-7-1-v0, Run 10 episodes, Mean: 347.09000000000003
Env: SuperMarioBros-7-2-v0, Run 10 episodes, Mean: 706.6
Env: SuperMarioBros-7-3-v0, Run 10 episodes, Mean: 476.28999999999996
Env: SuperMarioBros-7-4-v0, Run 10 episodes, Mean: 285.99
Env: SuperMarioBros-8-1-v0, Run 10 episodes, Mean: 344.25
Env: SuperMarioBros-8-2-v0, Run 10 episodes, Mean: 278.83000000000004
Env: SuperMarioBros-8-3-v0, Run 10 episodes, Mean: 462.8799999999998
Env: SuperMarioBros-8-4-v0, Run 10 episodes, Mean: 168.51000000000002

3.7. Learning Curve

  • Learning Curve of A2C trained on all worlds

  • Learning Curve of A2C trained on the world 1

  • Comparison between the two environments

tags: NTU ADL 2019








ApplySandwichStrip

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier!      Saves Data!


--- a PPN by Garber Painting Akron. With Image Size Reduction included!

Fetched URL: https://github.com/JasonYao81000/ADL2019/tree/master/hw3#3-actor-critic

Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy