Reinforcement learning is a subfield of machine learning that deals with how software agents ought to take actions in an environment to maximize some notion of cumulative reward. Reinforcement learning algorithms have been applied successfully to problems like checkers, backgammon, and other board games, to bicycle balancing, networked control systems, robot motion planning, and protein folding.
The Checkers player controversy motivated the development of more general Reinforcement Learning methods that can better learn policies for large and difficult problems. These methods are analogous to the methods used by humans when they try to figure out how to do something they have never done before. The goal is to find a good policy through trial and error, starting from complete ignorance about the task. The idea is to learn by exploration, using feedback from the environment to guide the learning process.
One approach to Reinforcement Learning is called Q-learning. Q-learning is a model-free learning algorithm that can be used to learn an optimal action-value function called a Q-function. The Q-function gives the expected return of taking a given action in a given state and following the optimal policy thereafter. The Q-function can be considered a map from states to actions, with the effort that maximizes the expected return being chosen at each state.
Q-learning can be used to solve the rlmujoco_hopper task, which is a 3D simulator of a one-legged hopping robot. The task is to get the robot to jump as high as possible. The reward is one if the robot jumps at least 10cm off the ground and 0 otherwise.
There are several different ways to formulate the Q-learning algorithm. One formulation is as follows:
1. Initialize the Q-function arbitrarily.
2. At each time step, select an action according to some exploration strategy (e.g., epsilon-greedy).
3. Take action and observe the next state and reward.
4. Update the Q-function according to the Bellman equation:
Q(s,a) = Q(s,a) + alpha * (r + gamma * max_a’ Q(s’,a’) – Q(s,a))
5. Repeat step 2 until it converges.
The Q-learning algorithm can be modified to incentivize exploration by using an epsilon-greedy action selection policy, where a random action is taken with probability epsilon, and the best action is taken with probability 1-epsilon. This modification can help the algorithm converge to the optimal policy more quickly.
There are also various ways to initialize the Q- function. A straightforward method is to set all values to zero. Another method is to set the values to a small random number.
There are many reinforcement learning algorithms, including Q-learning, SARSA, and TD learning. Q-learning is a model-free learning algorithm that can be used to learn an optimal action-value function called a Q-function. The Q-function gives the expected return of taking a given action in a given state and following the optimal policy thereafter. The Q-function can be considered a map from states to actions, with the action that maximizes the expected return being chosen at each state.
SARSA is another model-free reinforcement learning algorithm that can be used to learn an optimal policy. SARSA stands for State-Action-Reward-State-Action. The idea behind SARSA is that the agent learns by trial and error, using feedback from the environment to guide its learning.
TD learning is a model-based reinforcement learning algorithm. TD stands for Temporal Difference. TD learning is a type of learning that is very effective in practice. The idea behind TD learning is that the agent can learn by observing the difference between consecutive states (i.e., the temporal difference).
There are many different flavors of TD learning, including SARSA, Q-learning, and TD($lambda$). SARSA is a TD learning algorithm that is similar to Q-learning but with a slight modification. In SARSA, the state-action pair chosen at each time step is based on the action chosen at the previous time step, rather than the action that maximizes the expected return.
Q-learning is a TD learning algorithm similar to SARSA but with a different update rule. In Q-learning, the state-action pair chosen at each time step is based on the action that maximizes the expected return rather than the action chosen at the previous time step.
TD($lambda$) is a TD learning algorithm that is similar to Q-learning but with a different update rule. In TD($lambda$), the update at each time step is a linear combination of
References:
https://en.wikipedia.org/wiki/Reinforcement_learning
https://en.wikipedia.org/wiki/Q-learning
https://en.wikipedia.org/wiki/SARSA
https://en.wikipedia.org/wiki/Temporal_difference_learninghttps://en.wikipedia.org/wiki/State-Action-Reward-State-Action