Reinforcement Learning

The dialogue state tracker or just state tracker (ST) in a goal-oriented dialogue system has the primary job of preparing the state for the agent. As we discussed in the previous part the agent needs a useful state to be able to make a good choice on what action to take. The ST updates its internal history of the dialogue by collecting both user and agent actions as they are taken. It also keeps track of all inform slots that have been contained in any agent and user actions thus far in the current episode. The state used by the agent is a numpy array made of information from the current history and the current informs of the ST.

Here is a short video of Orso's minimal path through his turf. Notice how he avoids troublesome water in favor of longer paths through grassy lands. In our Half Day Hands-on Training At ODSC West in San Francisco we will show you more details on how you can use reinforcement learning practically. Using Colab notebooks we will model problems as a simulation environment (Orso's world) and train your agent (Orso himself) to learn a good strategy.

Codes and demo are available. This article explores what are states, actions and rewards in reinforcement learning, and how agent can learn through simulation to determine the best actions to take in any given state. After a long day at work, you are deciding between 2 choices: to head home and write an article or hang out with friends at a bar. If you choose to hang out with friends, your friends will make you feel happy; whereas heading home to write an article, you'll end up feeling tired after a long day at work. In this example, enjoying yourself is a reward and feeling tired is viewed as a negative reward, so why write articles?

Typically, a RL setup is composed of two components, an agent and an environment. Then environment refers to the object that the agent is acting on (e.g. the game itself in the Atari game), while the agent represents the RL algorithm. The environment starts by sending a state to the agent, which then based on its knowledge to take an action in response to that state. After that, the environment send a pair of next state and reward back to the agent. The agent will update its knowledge with the reward returned by the environment to evaluate its last action.

A6 months old baby won't even notice if a toy truck drives off a platform and seems to fly in the air. However, if the same experiment is repeated 2 to 3 months later, the baby will immediately identify that something is wrong. This means that the baby has already learned the concept of gravity. "Nobody tells a baby that objects are supposed to fall," said the chief AI scientist at Facebook and a professor at NYU, Dr. Yann LeCun, during a webinar organized by the Association for Computing Machinery, an industry body. Because babies do not have very sophisticated motor control, LeCun hypothesizes, "a lot of what they learn about the world is through observation."

To operate successfully in a complex and changing environment, learning agents must be able to acquire new skills quickly. Humans display remarkable skill in this area -- we can learn to recognize a new object from one example, adapt to driving a different car in a matter of minutes, and add a new slang word to our vocabulary after hearing it once. Meta-learning is a promising approach for enabling such capabilities in machines. In this paradigm, the agent adapts to a new task from limited data by leveraging a wealth of experience collected in performing related tasks. For agents that must take actions and collect their own experience, meta-reinforcement learning (meta-RL) holds the promise of enabling fast adaptation to new scenarios.

Then environment refers to the object that the agent is acting on (e.g. the game itself in the Atari game), while the agent represents the RL algorithm. The environment starts by sending a state to the agent, which then based on its knowledge to take an action in response to that state. After that, the environment send a pair of next state and reward back to the agent. The agent will update its knowledge with the reward returned by the environment to evaluate its last action. The loop keeps going on until the environment sends a terminal state, which ends to episode.

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Last week talked about Reinforcement Learning, how it's been used in real-world applications today some of the components and trade-off we ought to make when we program an agent to learn from its environment. You can check the post here. Today's post will be a short one as we focus only on the "DEEP" part of Deep Reinforcement Learning. As you might have already guessed, Deep Reinforcement Learning is just a variant of Reinforcement Learning, so everything we learn in previous Wednesday's post clearly holds and applies. However, I want to shed some light on the differences between DRL and traditional RL, so I think you'll find this article quite useful.