Accumulating eligibility trace - An accumulating eligibility trace is a temporary learning variable that grows when a state, state-action pair or feature is activated and decays between...
Action space - An action space is the set of actions an agent can choose from in a decision-making environment.
Actor-Critic Methods - Actor-critic methods are reinforcement-learning methods that learn both a policy, called the actor, and a value estimate, called the critic.
Advantage function - The advantage function measures how much better or worse an action's expected return is than the expected return from following a particular policy at the...
Adversarial inverse reinforcement learning - Adversarial inverse reinforcement learning, commonly abbreviated AIRL, is a method for learning a reward function from expert demonstrations using an...
Afterstate - An afterstate is the situation immediately after the known effects of an agent's action have occurred, before the remaining environment response is resolved.
Asynchronous Advantage Actor-Critic - Asynchronous Advantage Actor-Critic (A3C) is a reinforcement-learning method in which multiple actor-learners interact with separate environment instances...
Asynchronous policy iteration - Asynchronous policy iteration is a family of dynamic-programming approaches that update policy and value information for selected states or portions of a...
Bayesian reinforcement learning - Bayesian reinforcement learning is a family of approaches that represent uncertainty about a decision problem using probability distributions and update...
Behavior-based robotics - Behavior-based robotics is an approach to robot control that builds overall activity from interacting behaviors, such as avoiding obstacles, following a...
Behavior tree - A behavior tree is a hierarchical structure for controlling how an agent or system carries out tasks. Its nodes organize conditions, actions and control flow.
Bellman residual - A Bellman residual measures the discrepancy between a proposed value function and the result of applying a Bellman update to it.
Bernoulli bandit - A Bernoulli bandit is a multi-armed bandit problem in which each chosen action produces a binary reward, commonly 1 for success and 0 for failure.
Clipped double-Q learning - Clipped double-Q learning uses the smaller of two action-value estimates when constructing a learning target.
Cloud robotics - Cloud robotics uses network-accessible computation, data, or services to support robots and automation systems.
Conservative policy iteration - Conservative policy iteration is a reinforcement-learning approach that improves a policy by mixing it with a candidate improvement instead of replacing it...
Conservative Q-learning - Conservative Q-learning (CQL) is an offline reinforcement-learning framework that discourages inflated action-value estimates when learning from a fixed...
Costmap - A costmap is a spatial representation that assigns navigation costs to locations.
Curse of horizon in off-policy evaluation - The curse of horizon in off-policy evaluation refers to the difficulty of estimating a policy's performance from another policy's data as decision sequences...
DAgger - DAgger, short for Dataset Aggregation, is an iterative imitation-learning algorithm.
Deep deterministic policy gradient - Deep deterministic policy gradient, or DDPG, is an off-policy actor-critic reinforcement-learning algorithm for continuous action spaces.
Differential return - Differential return is a reward-based quantity used in continuing, average-reward reinforcement learning.
Differential value function - A differential value function describes expected future reward relative to a policy's long-run average reward in a continuing task.
Discounted state-action occupancy measure - A discounted state-action occupancy measure describes how much a policy visits each state-action pair over time, with later visits receiving less weight.
Dominating policy - A dominating policy, in the Markov decision-process sense used here, is a policy whose expected return is at least as high as that of every alternative...
Double deep Q-network - A double deep Q-network, usually called Double DQN, is a reinforcement-learning method that separates choosing a promising next action from estimating that...
Double-sampling problem in Bellman-error minimization - The double-sampling problem in Bellman-error minimization is the difficulty of obtaining an unbiased gradient estimate when that gradient contains a product...
DualDICE - DualDICE is a reinforcement-learning method for estimating corrections between a target policy's discounted state-action distribution and the distribution...
Dueling deep Q-network - A dueling deep Q-network is a Q-value network architecture that combines a shared state representation with two output streams: a scalar value stream and an...
Dutch eligibility trace - A Dutch eligibility trace is a recursively updated feature-memory vector used in reinforcement-learning algorithms such as true online TD(lambda).
Dyna-Q - Dyna-Q is a reinforcement-learning approach that integrates direct Q-learning, model learning and planning.
Dyna-Q with exploration bonus (Dyna-Q+) - Dyna-Q with exploration bonus, usually called Dyna-Q+, extends Dyna-Q by encouraging the agent to revisit state-action pairs that have gone untried for a...
Eligibility trace - An eligibility trace is a temporary memory used by a learning algorithm to determine which recently active states, actions or features should be updated...
Emphatic temporal-difference learning - Emphatic temporal-difference learning is a family of value-prediction methods that changes the emphasis placed on updates to address instability in...
Epsilon greedy policy - An epsilon greedy policy is a decision rule that usually chooses an action with the highest current estimated value but sometimes explores through random...
Evolutionary robotics - Evolutionary robotics applies evolutionary search methods to robot controllers, physical designs, or both.
Expected SARSA - Expected SARSA is a temporal-difference method that updates an action-value estimate using the policy-weighted average of the next state's action values.
Exploring starts - Exploring starts is an assumption or sampling arrangement in episodic reinforcement learning under which every relevant state-action pair has a nonzero...
GenDICE - GenDICE, short for Generalized stationary Distribution Correction Estimation, is a method for estimating distribution corrections from offline transition data.
Generative adversarial imitation learning - Generative adversarial imitation learning, or GAIL, learns a policy from expert demonstrations by training it against a discriminator.
Global costmap - A global costmap is a spatial cost representation used to support planning a route across an operating area.
Gradient bandit algorithm - A gradient bandit algorithm learns action preferences and converts them into action-selection probabilities, typically with a softmax function.
Gradient temporal-difference learning - Gradient temporal-difference learning refers to methods that estimate value functions by following gradients of carefully defined temporal-difference...
Greedy policy - A greedy policy chooses an action with the highest current estimated value at each decision point.
GTD2 - GTD2 is a gradient temporal-difference algorithm for learning a linear value-function approximation using a primary parameter vector and an auxiliary vector.
Hierarchical reinforcement learning - Hierarchical reinforcement learning is an approach to learning sequential decisions at more than one level of abstraction.
Human - robot collaboration - Human - robot collaboration is coordinated activity in which people and robots contribute to a shared task or goal.