From: lexfridman
Deep reinforcement learning (DRL) is a subset of machine learning that marries the decision-making prowess of reinforcement learning with the function approximation capabilities of deep learning. The following article delves into the central methodologies embedded in DRL, explaining the foundational concepts and techniques that underlie this branch of artificial intelligence.
Reinforcement Learning and Deep Reinforcement Learning
Reinforcement learning is a branch of machine learning focused on how agents take action in an environment to maximize a cumulative reward. The fundamental concept rests on agents interacting with an environment in unpredictable ways, aiming to maximize a defined reward over time—a general approach applicable to numerous goal-oriented tasks [00:01:06].
Deep reinforcement learning, specifically, involves leveraging neural networks as function approximators within the RL paradigm. Neural networks in DRL serve to approximate policies, value functions, or dynamics models, which can be quite challenging as it is not always clear what specific function should be approximated [00:01:42].
Learn More
Learn about the basics of DRL in our introduction_to_deep_reinforcement_learning article.
Key Techniques in Deep Reinforcement Learning
Policy Gradient Methods
Policy gradient methods focus on directly optimizing the policy that dictates the action an agent should take in various states of the environment. The core idea behind policy gradients is to increase the probability of taking beneficial actions that contribute to higher cumulative rewards by adjusting policy parameters. These methods are often advantageous due to their straightforward approach in dealing with scenarios where policies need to be continuous or stochastic [00:00:30].
The algorithm process typically involves executing a series of episodes using the current policy to gather trajectories, estimating rewards and policy gradients, and then adjusting the policy iteratively. For more advanced problem settings where variance is prominent in gradient estimates, strategies like using baselines to subtract expected rewards and employing discounts can significantly stabilize the learning process [00:41:57].
Application and Exploration
Policy gradient methods have been successfully applied to dynamic environments such as robotics and 3D gaming.
Q-Learning and Value-based Methods
Q-Learning and other value-based methods revolve around estimating a value function that indicates the expected utility or return of a state-action pair, assisting in identifying the optimal policy indirectly. Through iterative algorithms, such as value iteration and policy iteration, these methods attempt to solve Markov Decision Processes by predicting future rewards from present actions [01:05:15].
In DRL, Q-learning can be supplemented with neural networks to handle large or continuous state spaces, known as deep Q-Learning (DQN). This involves using techniques such as experience replay and the use of target networks to stabilize training.
Challenges in Deep Reinforcement Learning
DRL methods inherit complexities from both neural networks and reinforcement learning making them sensitive to hyperparameters, architecture design, and sample efficiency [01:18:06]. Further, determining the optimal exploration-exploitation trade-off without exhaustive tuning remains an open challenge.
The exploration problem can be exacerbated in environments where rewards are sparse or delayed, making it difficult for the agent to discern which actions led to which outcomes. Research into hierarchical reinforcement learning aims to address these issues, by introducing frameworks for managing both long and short-term decision making processes [01:53:51].
Future Directions
Discover more about future research areas in our article on challenges_and_future_directions_in_reinforcement_learning.
In conclusion, understanding and mastering the core techniques of deep reinforcement learning presents potent opportunities to solve complex dynamic problems, provided the nuances and limitations intrinsic to the methods are skillfully mitigated.