kengz

The HEX color for Atom highlighter theme: close to Base16 but lighter.
bgdark: #2b303b
red: #e26a78
cyan: #78dbf9
green: #8bdb97
purple: #d59cf6
orange: #dbad83

kengz

# First create a Google Compute Engine (GCE) VM instance, then SSH into the VM.
# 1. update everything
sudo apt-get update

# 2. install these 4 things
sudo apt-get install python g++ make checkinstall

# 3. make a src directory and go into it
mkdir ~/src && cd $_

kengz

Global Hackers Missions
[ Feel free to contribute and sign your own contribution! Separate by “Insert > Horizontal line”. See example below. ]
[ Anyone can edit this document, so be responsible. Of course we back this up somewhere else too 

###

This page is for doers who dare to, and for builders who want to, make a dent in the universe.

Do's: Be open minded, be dare, believe. 	
Don’t be butt-hurt, don’t be evil.

kengz

Keybase proof

I hereby claim:

• I am kengz on github.
• I am kengz (https://keybase.io/kengz) on keybase.
• I have a public key ASCZnM1pXJ3J3XW9-Nk2h22qWrep6ra1BfxuN6YBZFTbggo

To claim this, I am signing this object:

kengz

    def post_body_init(self):
        '''Initializes the part of algorithm needing a body to exist first.'''
        self.body = self.agent.nanflat_body_a[0]  # single-body algo
        # create the extra replay memory for SIL
        memory_name = self.memory_spec['sil_replay_name']
        MemoryClass = getattr(memory, memory_name)
        self.body.replay_memory = MemoryClass(self.memory_spec, self, self.body)
        self.init_algorithm_params()
        self.init_nets()
        logger.info(util.self_desc(self))

kengz

    def train_shared(self):
        '''
        Trains the network when the actor and critic share parameters
        '''
        if self.to_train == 1:
            # onpolicy a2c update
            a2c_loss = super(SIL, self).train_shared()
            # offpolicy sil update with random minibatch
            total_sil_loss = torch.tensor(0.0)
            for _ in range(self.training_epoch):

kengz

# init singleton agent and env
self.env = make_env(self.spec)
body = Body(self.env, self.spec['agent'])
self.agent = Agent(self.spec, self.info_space, body=body)

kengz

def run_episode(self):
    self.env.clock.tick('epi')
    reward, state, done = self.env.reset()
    self.agent.reset(state)
    while not done:
        self.env.clock.tick('t')
        action = self.agent.act(state)
        reward, state, done = self.env.step(action)
        self.agent.update(action, reward, state, done)
    self.agent.body.log_summary()

kengz

def run(self):
    while self.env.clock.get('epi') <= self.env.max_episode:
        self.run_episode()
    self.data = analysis.analyze_session(self)  # session fitness
    self.close()
    return self.data

kengz

'''
Demo: SLM Lab as pip module for lightweight usecases

Installation:

1. Clone SLM-Lab
```
git clone https://github.com/kengz/SLM-Lab.git
cd SLM-Lab
```