Policy search algorithms and "normal" reinforcement learning (RL) algorithms

Policy Search Algorithms versus"Normal" RL Algorithms
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Policy search algorithms and "normal" reinforcement learning (RL) algorithms differ in their approach to solving reinforcement learning problems, particularly in how they learn and represent the policy.

Table 3658. Policy search algorithms versus"normal" RL algorithms.

	Reinforcement Learning (RL)	Policy Search Algorithms
First Thing	To learn or approximate the value function V^, then use this V^ to figure out what is π^, which is indirect way of getting policy π^	Try to find good policy π^* directly without the extra steps
Direct Policy Optimization vs. Value Function Estimation	In traditional RL, many algorithms focus on estimating the value function, which assigns a value to each state or state-action pair. The value function represents the expected cumulative reward the agent can obtain from a given state or state-action pair.	Instead of estimating the value function, policy search algorithms directly optimize the policy. They aim to find the best policy, a mapping from states to actions, without explicitly modeling the underlying dynamics of the environment or estimating value functions.
Exploration vs. Exploitation	RL algorithms often involve a trade-off between exploration and exploitation. Agents need to explore the environment to discover the best actions, but they also need to exploit their current knowledge to maximize immediate rewards.	Policy search methods naturally incorporate exploration in the learning process because they often involve stochastic policies. By sampling different actions from the policy, the agent explores the space of possible actions, facilitating the discovery of effective strategies.
Representation of Policy	Traditional RL methods might represent policies implicitly through action-value functions (Q-functions) or state-value functions (V-functions). The agent selects actions based on the estimated values of different actions or states.	Policy search algorithms explicitly parameterize the policy and optimize these parameters directly. This is often done using optimization techniques, such as gradient ascent, to maximize the expected cumulative reward with respect to the policy parameters.
Model-Based vs. Model-Free	RL algorithms can be categorized as model-based or model-free. Model-based algorithms aim to learn a model of the environment and use it to plan actions, while model-free algorithms directly learn the policy or value function without explicitly modeling the environment.	Policy search methods are generally model-free. They do not explicitly model the transition dynamics of the environment and focus on learning a policy that directly maps states to actions.
Sample Efficiency and Computational Complexity	Some RL algorithms, particularly model-free ones, may require a significant amount of data and time to learn an effective policy, especially in complex environments.	While policy search methods can be sample-inefficient in some cases, they are often applied in scenarios where the system's dynamics are complex, and explicit modeling is challenging. However, they may require more data and computational resources compared to some model-based approaches.
Logistic Regression
Applications	The choice between regular Reinforcement Learning (RL) methods and Policy Search Algorithms depends on various factors, including the nature of the problem, the characteristics of the environment, and the specific requirements of the task at hand.
Applications	Known Dynamics: If we have a good understanding of the dynamics of the environment and can accurately model the transition probabilities and rewards, traditional RL methods like Q-learning or value iteration might be suitable. Continuous Action Spaces: Regular RL methods are often more naturally suited for problems with continuous action spaces. Algorithms like DDPG (Deep Deterministic Policy Gradients) and SAC (Soft Actor-Critic) can handle continuous actions efficiently. Value Function Estimation: If our goal is to estimate the value function (state-values or action-values) to guide decision-making, traditional RL methods like Q-learning or SARSA might be appropriate. Exploration-Exploitation: Regular RL algorithms inherently balance exploration and exploitation, making them suitable for tasks where the agent needs to discover optimal actions in uncertain environments. Known State Space: If the state space is well-defined and not too large, traditional RL algorithms can be effective. Model-based methods might also be considered if the environment's dynamics are known.	Unknown or Complex Dynamics: If the dynamics of the environment are complex, unknown, or challenging to model accurately, policy search methods can be more suitable as they directly optimize the policy without relying on an explicit model. Stochastic Policies: When dealing with problems that inherently involve uncertainty or stochasticity, policy search methods, which can represent and optimize stochastic policies, might be more appropriate. High-Dimensional Action Spaces: Policy search algorithms, particularly those based on deep learning, can handle high-dimensional action spaces more effectively than traditional RL methods. Sample Efficiency: In scenarios where collecting data is expensive or time-consuming, policy search methods may be more sample-efficient as they can learn from fewer interactions with the environment. Robotics and Control: Policy search algorithms are often applied in robotics and control tasks, where fine-tuning the policy directly on a physical system is common. Black-Box Optimization: If our problem can be treated as a black-box optimization task, where you can sample actions and observe the resulting rewards, policy search methods might be appropriate.

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