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reinforcement learning project for fun :)
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README.md

Code Sketch

The library exports two global variables: R, and RL. The former contains various kinds of utilities for building expression graphs (e.g. LSTMs) and performing automatic backpropagation. The RL object contains the current implementations:

  • RL.DPAgent for finite state/action spaces with environment dynamics
  • RL.TDAgent for finite state/action spaces
  • RL.DQNAgent for continuous state features but discrete actions

A typical usage might look something like:

// create an environment object
var env = {};
env.getNumStates = function() { return 8; }
env.getMaxNumActions = function() { return 4; }

// create the DQN agent
var spec = { alpha: 0.01 } // see full options on DQN page
agent = new RL.DQNAgent(env, spec); 

setInterval(function(){ // start the learning loop
  var action = agent.act(s); // s is an array of length 8
  //... execute action in environment and get the reward
  agent.learn(reward); // the agent improves its Q,policy,model, etc. reward is a float
}, 0);
``
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