View source for Lifelong Latent Actor-Critic (LILAC)

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* [[Lifelong Learning]]
* [[Reinforcement Learning (RL)]]
** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning
** [[Markov Decision Process (MDP)]]
** [[State-Action-Reward-State-Action (SARSA)]]
** [[Q Learning]]
*** [[Deep Q Network (DQN)]]
** [[Deep Reinforcement Learning (DRL)]] DeepRL
** [[Distributed Deep Reinforcement Learning (DDRL)]]
** [[Evolutionary Computation / Genetic Algorithms]]
** [[Actor Critic]]
*** [[Asynchronous Advantage Actor Critic (A3C)]]
*** [[Advanced Actor Critic (A2C)]]
*** Lifelong Latent Actor-Critic (LILAC)
** [[Hierarchical Reinforcement Learning (HRL)]]
* [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]


Lifelong Latent Actor-Critic (LILAC) is an off-policy reinforcement learning algorithm that can reason about and tackle lifelong non-stationarity. It leverages [[latent]] variable models to learn a representation of the environment from current and past experiences, and performs off-[[policy]] [[Reinforcement Learning (RL)]] with this representation. LILAC is based on the following three key ideas:

* <b>Lifelong learning</b>: LILAC can learn from experiences gathered over time, even if the environment changes. This is because LILAC learns a representation of the environment that is invariant to small changes.
* <b>Latent variable models</b>: LILAC uses [[latent]] variable models to learn a compact representation of the environment. This representation captures the underlying structure of the environment, which makes it easier for LILAC to learn to act optimally.
* <b>Maximum entropy policy</b>: LILAC uses a maximum entropy [[policy]], which is a [[policy]] that is as diverse as possible. This makes LILAC more robust to changes in the environment, and also helps it to explore new and potentially better [[Policy|policies]].

LILAC has been shown to outperform state-of-the-art [[Reinforcement Learning|RL algorithms]] on a variety of simulated environments that exhibit lifelong non-stationarity. For example, LILAC was able to learn to navigate a maze that changes over time, and to learn to play a game against an opponent who is constantly adapting. LILAC is a promising [[Reinforcement Learning|RL algorithm]] that has the potential to be used in a wide range of applications, such as robotics, autonomous driving, and finance. Here are some examples of how LILAC could be used in the real world:

* A robot that uses LILAC could learn to navigate a changing environment, such as a warehouse where the shelves are constantly being moved around.
* A self-driving car that uses LILAC could learn to drive in a variety of different conditions, such as traffic, road construction, and weather changes.
* A financial trading agent that uses LILAC could learn to trade in a market that is constantly changing.

= LILAC @ SAIL =
Researchers from [https://ai.stanford.edu/ Stanford AI Lab (SAIL)] have devised a method to deal with data and environments that change over time in a way that outperforms some leading approaches to reinforcement learning. Lifelong Latent Actor-Critic, aka LILAC, uses latent variable models and a maximum entropy policy to leverage past experience for better sample efficiency and performance in dynamic environments. [https://venturebeat.com/2020/07/01/stanford-ai-researchers-introduce-lilac-reinforcement-learning-for-dynamic-environments/ Stanford AI researchers introduce LILAC, reinforcement learning for dynamic environments | Khari Johnson - VentureBeat]



== Continuous Action ==

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