Reinforcement Learning

An agent observes a state or observation, chooses an action and receives a reward and subsequent observation. A policy specifies action selection; value functions estimate future return. Methods can learn values, optimize policy parameters or use a model of environment dynamics. Discounting and the planning horizon determine how future rewards contribute to the objective. Exploration is needed to learn about actions, while sequential credit assignment determines which earlier decisions contributed to later outcomes. Unlike a simple bandit, reinforcement learning typically models how actions change future situations. A reward is a mathematical proxy for the goal, not proof that the learned behavior achieves the desired outcome.

Specify observations, actions, episode boundaries and reward components, then check what the agent can actually observe. Choose an algorithm appropriate to discrete or continuous actions and available interaction data. Establish heuristic baselines, monitor learning stability and evaluate policies across seeds and environment conditions. Inspect trajectories for unsafe shortcuts or reward exploitation. The output is a policy plus evidence of its behavior, including how evaluation differs from training and what constraints prevent harmful exploration or untested actions in the deployment setting.

Sample requirements can be substantial, and simulation success may not transfer when real dynamics differ. Reward design can encourage behavior that satisfies the score while undermining the intended goal. Offline data may not support evaluation of actions rarely taken by the logging policy. Partial observability and changing environments complicate learning. Reinforcement learning is distinct from supervised imitation and from contextual bandits; those methods may be sufficient when long-term effects are absent or cannot be safely explored.