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approximate reinforcement learning

Approximate reinforcement learning refers to a framework within machine learning where algorithms use function approximation techniques to estimate value functions, policies, or system models when solving sequential decision-making problems with large or continuous state and action spaces. In contrast to exact tabular reinforcement learning, which requires storing and updating discrete values for every individual state-action pair, approximate methods leverage parametric or non-parametric models, such as linear combinations of features or deep neural networks, to generalize knowledge across similar states. This approach allows an agent to discover near-optimal behavioral policies within tractable computational time and memory constraints, making reinforcement learning scalable to complex environments where exact solutions are mathematically or computationally infeasible.

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Approximately Optimal Approximate Reinforcement Learning

Approximately Optimal Approximate Reinforcement Learning

S. Kakade, John Langford

OrganizationsCarnegie Mellon UniversityUniversity College London

Why you should read this

Proposes conservative policy iteration, an algorithm that uses mixture policy updates to guarantee monotonic performance improvement and fast convergence to a near-optimal policy with complexity independent of state space size.

In order to solve realistic reinforcement learning problems, it is critical that approximate algorithms be used. In this paper, we present the conservative policy iteration algorithm which finds an “approximately” optimal policy, given access to a restart distribution (which draws the next state from a particular distribution) and an approximate greedy policy chooser. Crudely, the greedy policy chooser outputs a policy that usually chooses actions with the largest state-action values of the current policy, ie it outputs an “approximate” greedy policy. This greedy policy chooser can be implemented using standard value function approximation techniques. Under these assumptions, our algorithm: (1) is guaranteed to improve a performance metric (2) is guaranteed to terminate in a “small” number of timesteps and (3) returns an “approximately” optimal policy. The quantified statements of (2) and (3) depend on the quality of the greedy policy chooser, but not explicitly on the the size of the state space.

Added

2026-09-25