Online: as data comes in, have more data with timing constraints.
Offline: you have a static dataset, acquire a large corpus as dataset and train a classifier.
https://stats.stackexchange.com/questions/897/online-vs-offline-learning
Some details need more research, but works for now!
MuJoCo is a advanced physical simulation tool, developed by Emo Todorov for Roboti LLC
mujoco-py is a glue to let people use MuJoCo in python, developed by OpenAI Robotics team
OpenAI Gym is a modular python package to provide environments for RL, developed by OpenAI
Get License
Install MuJoCo
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/.mujoco/mjpro150/bin"
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib/nvidia-384"Test MuJoCo
export MUJOCO_PY_MJPRO_PATH="/.mujoco/mjpro150"
export MUJOCO_PY_MUJOCO_PATH="/.mujoco"
export MUJOCO_PY_MJKEY_PATH="/.mujoco/mjkey.txt"Recommend installing this after MuJoCo and mujoco-py.
import gym
env = gym.make('Humanoid-v2')
env.reset()
for _ in range(1000):
env.render()
action = env.action_space.sample()
env.step(action)https://deeprobotics.wordpress.com/2018/01/23/installing-mujoco-and-integrating-it-with-python-on-ubuntu/
https://github.com/openai/mujoco-py/issues/190
Prediction: predicting estimated total return
Control: maximizing estimated total return
Dynamic Programming Methods
Policy Evaluation: prediction (Bellman Expectation Equation) with an error bound of epsilon
Policy Iteration: policy evaluation (Bellman Expectation Equation) + greedy policy update until convergence
Value Iteration: run Bellman Optimality Equation to maximize return
* DP is is a model-based approach, where we have the complete knowledge of environment. such as transition probabilities and rewards
Monte Carlo Methods & Temporal-Difference Methods
| On-policy | Off-policy | |
| Monte Carlo | Monte Carlo | |
| Temporal-Difference | SARSA | Q-Learning |
* MC and TD are model-free approaches, where we just focus on figuring out the value functions directly from the interactions with the environment
Biggest breakthrough with one of these method, I think, was probably DQN which gave a good nudge which now lead to great interest in reinforcement learning.
All the above methods are value-based method. V is state-value and Q is action-value, and we have been using them to build policy from it. DQN is also value-based method which is just augmentation of neural network on the target and behavior function.
Using policy-based method has several advantages: better convergence, well-suited for continuous action, and possibility of stochastic policy. On the other hand, it can fall into local optimum and is rather inefficient to evaluate and has high variance.
Recent research seem to have been making policy-based methods more sample efficient, lower variance, and making them reach better optima.
Following are some of the categories.
Finite Difference Policy Gradient
This is a numerical method where we tweak each parameter by epsilon to evaluate derivatives, taking n evaluations to compute policy gradient in n dimensions.
Monte Carlo Policy Gradient (REINFORCE)
This is the analytical way to get the gradient, where we take the gradient of the objective function directly (assuming the policy is differentiable).
We derive it the following way
[include derivation]
One more design choice to be made is on expressing stochastic policy. It is done using either (1) softmax or (2) gaussian
Actor-Critic Policy Gradient
Critic approximate Q function (with parameter w) and Actor approximates policy (with parameter theta)
Sutton’s Book: http://incompleteideas.net/book/the-book.html
PPO paper: https://arxiv.org/pdf/1707.06347.pdf
Policy Gradient Algorithms: https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html#reinforce
Byung Hoon Ahn 