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Deep reinforcement learning (Deep RL) combines reinforcement learning (RL) and deep learning. It has shown remarkable success in complex tasks previously unimaginable for a machine. Deep RL has achieved human-level or superhuman performance for many two-player or multi-player games. Such achievements with popular games are significant because they show the potential of deep RL in various complex and diverse tasks based on high-dimensional inputs.
This article introduces deep reinforcement learning models, algorithms, and techniques. It will cover a brief history of deep RL, a basic theoretical explanation of deep RL networks, state-of-the-art deep RL algorithms, major application areas, and the future research scope in the field.
Reinforcement learning offers a theoretical framework, grounded in psychology and neuroscience, for agents to optimize their interaction with environments. However, real-world applications demand agents to extract relevant information from complex sensory inputs efficiently. As neural data shows, humans excel in this task by integrating reinforcement learning with hierarchical sensory processing systems. While reinforcement learning has shown promise, its practical use has been confined to domains with handcrafted features or fully observable, low-dimensional states. Overcoming these limitations remains challenging for extending its applicability to more complex environments, giving rise to deep RL techniques, i.e., combining RL with deep learning techniques.
One of the first successful applications of RL with neural networks was TD-Gammon, a computer program developed in 1992 for playing backgammon. 2013 DeepMind showed impressive learning results using deep RL to play Atari video games. The computer player is a neural network trained using a deep RL algorithm, a deep version of Q-learning called deep Q-networks (DQN), with the game score as the reward. It outperforms all previous approaches on six games and surpasses a human expert on three. In 2017, DeepMind researchers introduced a generalized version of AlphaGo, which they named AlphaZero. AlphaZero has achieved within twenty-four hours a superhuman level of play in the games of chess and shogi (i.e., Japanese chess) as well as Go, and defeated a world-champion program in each case.
The basic architecture of a Deep RL framework involves the interaction between the agent and the environment. The agent learns to make decisions through trial and error, guided by rewards received from the environment. Using deep neural networks allows Deep RL agents to handle high-dimensional observation spaces and learn complex decision-making policies directly from raw sensory inputs.
A thorough overview of some of the successful Deep RL algorithms:
Deep Q-Network (DQN): Introduced in 2015 by DeepMind, DQN was one of the first successful applications of deep learning to RL. It utilizes a deep neural network to approximate the Q-function, enabling the agent to learn value-based policies directly from raw sensory inputs. Experience replay and target networks stabilize training and improve sample efficiency. The agent achieved superhuman performance in playing a variety of Atari 2600 video games.
Deep Deterministic Policy Gradient (DDPG): Proposed in 2015 by researchers from Google DeepMind, DDPG is designed for continuous action spaces. It combines deep Q-learning with deterministic policy gradients to learn value and policy functions simultaneously. It utilizes an actor-critic architecture, where the actor-network learns the policy, and the critic network learns the Q-function. It is particularly effective in tasks with continuous control, such as robotic manipulation and locomotion.
Proximal Policy Optimization (PPO): Introduced by OpenAI in 2017, PPO is a simple yet effective policy gradient method for training deep RL agents. Addresses the problem of unstable policy updates by constraining the policy update using a clipped surrogate objective. It balances sample efficiency and ease of implementation, making it widely used in practice. It is known for its robustness and stability across various environments and applications.
Trust Region Policy Optimization (TRPO): Proposed by OpenAI in 2015, TRPO aims to improve the stability of policy gradient methods by enforcing a trust region constraint. Updates the policy in small steps to ensure that the new policy does not deviate too much from the previous one, thereby avoiding large policy changes that may lead to performance degradation.
Soft Actor-Critic (SAC): Introduced by researchers from Berkeley Artificial Intelligence Research in 2018, SAC is an off-policy actor-critic algorithm that optimizes a stochastic policy. It maximizes a trade-off between the expected return and entropy of the policy, leading to exploration in high-dimensional action spaces. SAC offers good sample efficiency and robustness, making it suitable for various tasks, including robotic and continuous control domains.
In conclusion, Deep RL merges reinforcement learning with deep learning, achieving remarkable success in complex tasks, including superhuman game performance. Key advancements like DQN, DDPG, PPO, TRPO, and SAC have propelled the field. These algorithms address high-dimensional inputs, continuous action spaces, and stability challenges. Deep RL’s potential spans diverse domains, promising further breakthroughs in AI.
Sources:
https://arxiv.org/pdf/1811.12560.pdf
https://www.nature.com/articles/nature14236
https://dl.acm.org/doi/pdf/10.1145/203330.203343
https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
https://www.nature.com/articles/nature16961
https://arxiv.org/pdf/1509.02971.pdf
https://arxiv.org/pdf/1707.06347.pdf
https://arxiv.org/pdf/1502.05477.pdf
https://arxiv.org/pdf/1801.01290.pdf
https://arxiv.org/pdf/1712.01815.pdf
https://arxiv.org/pdf/1312.5602.pdf
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