Reinforcement Learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context use dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.

Introduction

Due to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics. In the operations research and control literature, reinforcement learning is called ''approximate dynamic programming,'' or ''neuro-dynamic programming.'' The problems of interest in reinforcement learning have also been studied in the theory of optimal control, which is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact computation, and less with learning or approximation, particularly in the absence of a mathematical model of the environment. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.

Basic reinforcement learning is modeled as a Markov decision process (MDP):

* a set of environment and agent states, ;
* a set of actions, , of the agent;
* $P_a(s,s')=\Pr(s_=s'\mid s_t=s, a_t=a)$ is the probability of transition (at time $t$) from state $s$ to state $s'$ under action $a$.
* $R_a(s,s')$ is the immediate reward after transition from $s$ to $s'$ with action $a$.

The purpose of reinforcement learning is for the agent to learn an optimal, or nearly-optimal, policy that maximizes the "reward function" or other user-provided reinforcement signal that accumulates from the immediate rewards. This is similar to processes that appear to occur in animal psychology. For example, biological brains are hardwired to interpret signals such as pain and hunger as negative reinforcements, and interpret pleasure and food intake as positive reinforcements. In some circumstances, animals can learn to engage in behaviors that optimize these rewards. This suggests that animals are capable of reinforcement learning.

A basic reinforcement learning agent AI interacts with its environment in discrete time steps. At each time , the agent receives the current state $s_t$ and reward $r_t$. It then chooses an action $a_t$ from the set of available actions, which is subsequently sent to the environment. The environment moves to a new state $s_$ and the reward $r_$ associated with the ''transition'' $(s_t,a_t,s_)$ is determined. The goal of a reinforcement learning agent is to learn a ''policy'': \pi: A \times S \rightarrow ,1, $\pi(a,s) = \Pr(a_t = a\mid s_t =s)$ which maximizes the expected cumulative reward.

Formulating the problem as an MDP assumes the agent directly observes the current environmental state; in this case the problem is said to have ''full observability''. If the agent only has access to a subset of states, or if the observed states are corrupted by noise, the agent is said to have ''partial observability'', and formally the problem must be formulated as a Partially observable Markov decision process. In both cases, the set of actions available to the agent can be restricted. For example, the state of an account balance could be restricted to be positive; if the current value of the state is 3 and the state transition attempts to reduce the value by 4, the transition will not be allowed.

When the agent's performance is compared to that of an agent that acts optimally, the difference in performance gives rise to the notion of '' regret''. In order to act near optimally, the agent must reason about the long-term consequences of its actions (i.e., maximize future income), although the immediate reward associated with this might be negative.

Thus, reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off. It has been applied successfully to various problems, including robot control, elevator scheduling, telecommunications, backgammon, checkers and Go (AlphaGo).

Two elements make reinforcement learning powerful: the use of samples to optimize performance and the use of function approximation to deal with large environments. Thanks to these two key components, reinforcement learning can be used in large environments in the following situations:
* A model of the environment is known, but an analytic solution is not available;
* Only a simulation model of the environment is given (the subject of simulation-based optimization);
* The only way to collect information about the environment is to interact with it.
The first two of these problems could be considered planning problems (since some form of model is available), while the last one could be considered to be a genuine learning problem. However, reinforcement learning converts both planning problems to machine learning problems.

Exploration

The exploration vs. exploitation trade-off has been most thoroughly studied through the multi-armed bandit problem and for finite state space MDPs in Burnetas and Katehakis (1997).

Reinforcement learning requires clever exploration mechanisms; randomly selecting actions, without reference to an estimated probability distribution, shows poor performance. The case of (small) finite MDPs is relatively well understood. However, due to the lack of algorithms that scale well with the number of states (or scale to problems with infinite state spaces), simple exploration methods are the most practical.

One such method is $\varepsilon$-greedy, where $0 < \varepsilon < 1$ is a parameter controlling the amount of exploration vs. exploitation. With probability $1-\varepsilon$, exploitation is chosen, and the agent chooses the action that it believes has the best long-term effect (ties between actions are broken uniformly at random). Alternatively, with probability $\varepsilon$, exploration is chosen, and the action is chosen uniformly at random. $\varepsilon$ is usually a fixed parameter but can be adjusted either according to a schedule (making the agent explore progressively less), or adaptively based on heuristics.

Algorithms for control learning

Even if the issue of exploration is disregarded and even if the state was observable (assumed hereafter), the problem remains to use past experience to find out which actions lead to higher cumulative rewards.

Criterion of optimality

Policy

The agent's action selection is modeled as a map called ''policy'':
:\pi: A \times S \rightarrow ,1/math>
:$\pi(a,s) = \Pr(a_t = a\mid s_t =s)$

The policy map gives the probability of taking action $a$ when in state $s$. There are also deterministic policies.

State-value function

The value function $V_\pi(s)$ is defined as the ''expected return'' starting with state $s$, i.e. $s_0 = s$, and successively following policy $\pi$. Hence, roughly speaking, the value function estimates "how good" it is to be in a given state.

:V_\pi(s) = \operatorname E \mid s_0 = s= \operatorname E\left sum_^\infty \gamma^t r_t\mid s_0 = s\right

where the random variable $R$ denotes the return, and is defined as the sum of future discounted rewards:

:$R=\sum_^\infty \gamma^t r_t,$

where $r_t$ is the reward at step $t$, \gamma \in discount-rate._Gamma_is_less_than_1,_so_events_in_the_distant_future_are_weighted_less_than_events_in_the_immediate_future.

The_algorithm_must_find_a_policy_with_maximum_expected_return._From_the_theory_of_MDPs_it_is_known_that,_without_loss_of_generality,_the_search_can_be_restricted_to_the_set_of_so-called_''stationary''_policies._A_policy_is_''stationary''_if_the_action-distribution_returned_by_it_depends_only_on_the_last_state_visited_(from_the_observation_agent's_history)._The_search_can_be_further_restricted_to_''deterministic''_stationary_policies._A_''deterministic_stationary''_policy_deterministically_selects_actions_based_on_the_current_state._Since_any_such_policy_can_be_identified_with_a_mapping_from_the_set_of_states_to_the_set_of_actions,_these_policies_can_be_identified_with_such_mappings_with_no_loss_of_generality.

__Brute_force_

The_ discount-rate._Gamma_is_less_than_1,_so_events_in_the_distant_future_are_weighted_less_than_events_in_the_immediate_future.

The_algorithm_must_find_a_policy_with_maximum_expected_return._From_the_theory_of_MDPs_it_is_known_that,_without_loss_of_generality,_the_search_can_be_restricted_to_the_set_of_so-called_''stationary''_policies._A_policy_is_''stationary''_if_the_action-distribution_returned_by_it_depends_only_on_the_last_state_visited_(from_the_observation_agent's_history)._The_search_can_be_further_restricted_to_''deterministic''_stationary_policies._A_''deterministic_stationary''_policy_deterministically_selects_actions_based_on_the_current_state._Since_any_such_policy_can_be_identified_with_a_mapping_from_the_set_of_states_to_the_set_of_actions,_these_policies_can_be_identified_with_such_mappings_with_no_loss_of_generality.

__Brute_force_

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