Coursework Hero is a genuine essay writing and homework help service. We understand that a shade of mistrust has covered the paper writing industry, Coursework Hero is a genuine essay writing and homework help service. We understand that a shade of mistrust has covered the paper writing industry, and we want to convince you of our loyalty 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
Reinforcement learning - Wikipedia
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.
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 MDPbecause many reinforcement learning algorithms for this context use dynamic programming techniques. Due to its generality, reinforcement learning is studied in many disciplines, such as game theorycontrol theoryoperations researchinformation theorysimulation-based optimizationmulti-agent systemsswarm intelligenceand 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 controlwhich is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact effective teaching behaviors positive inforcement thesis phd, and less with learning or approximation, particularly in the absence of a mathematical model of the environment.
In economics and game theoryreinforcement learning may be used to explain how equilibrium may arise under bounded rationality. Basic reinforcement is modeled as a Markov decision process MDP :. 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. Formulating effective teaching behaviors positive inforcement thesis phd problem as an MDP assumes the agent directly observes the current environmental state; in this case the problem is said to have full observability, effective teaching behaviors positive inforcement thesis phd.
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 observabilityand formally the problem must be formulated as a Partially observable Markov decision process, effective teaching behaviors positive inforcement thesis phd.
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. 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[7] elevator scheduling, telecommunicationsbackgammoncheckers [8] 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:. The first two of these problems could be considered planning problems since some form of model is availablewhile the last one could be considered to be a genuine learning problem.
However, reinforcement learning converts both planning problems to machine learning problems. 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 Reinforcement learning requires clever exploration mechanisms; randomly selecting actions, without reference to an estimated probability distribution, shows poor performance.
The case of small finite Markov decision processes 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 spacessimple exploration methods are the most practical. Even if the issue of exploration is disregarded and even if the state was observable assumed hereafterthe problem remains to use past experience to find out which actions lead to higher cumulative rewards.
Hence, roughly speaking, the value function estimates "how good" it is to be in a given state. 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. The brute force approach entails two steps:. One problem with this is that the number of policies can be large, or even effective teaching behaviors positive inforcement thesis phd. Another is that the variance of the returns may be large, which requires many samples to accurately estimate the return of each policy.
These problems can be ameliorated if we assume some structure and allow samples generated from one policy to influence the estimates made for others. The two main approaches for achieving this are value function estimation and direct policy search. Value function approaches attempt to find a policy that maximizes the return by maintaining a set of estimates of expected returns for some policy usually either the "current" [on-policy] or the optimal [off-policy] one.
These methods rely on the theory of Markov decision processes, where optimality is defined in a effective teaching behaviors positive inforcement thesis phd that is stronger than the above one: A policy is called optimal if it achieves the best-expected return from any initial state i.
Again, an optimal policy can always be found amongst stationary policies. A policy that achieves these optimal values in each state is called optimal. Although state-values suffice to define optimality, it is useful to define action-values. In summary, the knowledge of the optimal action-value function alone suffices to know how to act optimally.
Assuming full knowledge of the MDP, the two basic approaches to compute the optimal action-value function are value iteration and policy iteration. Computing these functions involves computing expectations over the whole state-space, which is impractical for all but the smallest finite MDPs.
In reinforcement learning methods, effective teaching behaviors positive inforcement thesis phd, expectations are approximated by averaging over samples and using function approximation techniques to cope with the need to represent value functions over large state-action spaces.
Monte Carlo methods can be used in an algorithm that mimics policy iteration. Policy iteration consists of two steps: policy evaluation and policy improvement. Monte Carlo is used in the policy evaluation step. Assuming for simplicity that the MDP is effective teaching behaviors positive inforcement thesis phd, that sufficient memory is available to accommodate the action-values and that the problem is episodic and after each episode a new one starts from some random initial state. This finishes the description of the policy evaluation step.
In practice lazy evaluation can defer the computation of the maximizing actions to when they are needed. The first problem is corrected by allowing the procedure to change the policy at some or all states before the values settle. This too may be problematic as it might prevent convergence. Most current algorithms do this, giving rise to the class of generalized policy iteration algorithms. Many actor-critic methods belong to this category. The second issue can be corrected by allowing trajectories to contribute to any state-action pair in them.
This may also help effective teaching behaviors positive inforcement thesis phd some extent with the third problem, although a better solution when returns have high variance is Sutton's temporal difference TD methods that are based on the recursive Bellman equation. Batch methods, effective teaching behaviors positive inforcement thesis phd, such as the least-squares temporal difference method, [15] may use the information in the samples better, while incremental methods are the only choice when batch methods are infeasible due to their high computational or memory complexity.
Some methods try to combine the two approaches. Methods based on temporal differences also overcome the fourth issue. In order to address the fifth issue, function approximation methods are used.
The algorithms then adjust the weights, instead of adjusting the values associated with the individual state-action pairs. Methods based on ideas from nonparametric statistics which can be seen to construct their own features have been explored. Value iteration can also be used as a effective teaching behaviors positive inforcement thesis phd point, giving rise to the Q-learning algorithm and its many variants.
The problem with using action-values is that they may need highly precise estimates of the competing action values that can be hard to obtain when the returns are noisy, though this problem is mitigated to some extent by temporal difference methods.
Using the so-called compatible function approximation method compromises generality and efficiency. Another problem specific to TD comes from their reliance on the recursive Bellman equation.
This can be effective in palliating this issue. An alternative method is to search directly in some subset of the policy space, in which case the problem becomes a case of stochastic optimization. The two approaches available are gradient-based and gradient-free methods. Defining the performance function by. Since an analytic expression for the gradient is not available, only a noisy estimate is available.
Such an estimate can be constructed in many ways, giving rise to algorithms such as Williams' REINFORCE method [17] which is known as the likelihood ratio method in the simulation-based optimization literature.
A large class of methods avoids relying on gradient information. These include simulated annealingcross-entropy search or methods of evolutionary computation. Many gradient-free methods can achieve in theory and in the limit a global optimum.
Policy search methods may converge slowly given noisy data. For example, this happens in episodic problems when the trajectories are long and the variance of the returns is large. Value-function based methods that rely on temporal differences might help in this case.
In recent years, effective teaching behaviors positive inforcement thesis phd, actor—critic methods have been proposed and performed well on various problems. Finally, all of the above methods can be combined with algorithms that first learn a model. For instance, the Dyna algorithm [21] learns a model from experience, and uses that to provide more modelled transitions for a value function, in addition to the real transitions.
Such methods can sometimes be extended to use of non-parametric models, such as when the transitions are simply stored and 'replayed' [22] to the learning algorithm.
There are other ways to use models than to update a value function. Both the asymptotic and finite-sample behaviors of most algorithms are well understood, effective teaching behaviors positive inforcement thesis phd. Algorithms with provably good online performance addressing the exploration issue are known.
Efficient exploration of MDPs is given in Burnetas and Katehakis For incremental algorithms, asymptotic convergence issues have been settled [ clarification needed ].
Temporal-difference-based algorithms converge under a wider set of conditions than was previously possible for example, when used with arbitrary, smooth function approximation.
Associative reinforcement learning tasks combine facets of stochastic learning automata tasks and supervised learning pattern classification tasks.
Positive Reinforcement for Online Classrooms
, time: 8:16Reinforcement learning - Wikipedia
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 Coursework Hero is a genuine essay writing and homework help service. We understand that a shade of mistrust has covered the paper writing industry, Coursework Hero is a genuine essay writing and homework help service. We understand that a shade of mistrust has covered the paper writing industry, and we want to convince you of our loyalty
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