We discuss the relative merits of optimistic and randomized approaches to exploration in reinforcement learning. Optimistic approaches presented in the literature apply an optimistic boost to the value estimate at each state-action pair and select actions that are greedy with respect to the resulting optimistic value f…
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48 results for “optimistic action”
Proposes Optimistic Pessimistically Initialised Q-Learning (OPIQ) for better exploration in RL.
problem Pessimistic initialisation of Q-values in deep RL leads to poor exploration performance.
method Augments pessimistically initialised Q-values with count-based bonuses to ensure optimism.
result OPIQ outperforms non-optimistic DQN variants in hard exploration tasks.
New algorithm reduces regret in sequential decision-making problems.
problem Balancing exploration and exploitation in online sequential decision problems.
method Variational Bayesian optimistic sampling (VBOS) for optimizing policies.
result VBOS achieves Bayesian regret for stochastic multi-armed bandits.
Optimistic Hedge achieves optimal regret bounds in two-player zero-sum games.
problem Achieving optimal regret bounds for optimistic Hedge in two-player zero-sum games.
method Refined regret analysis and optimization problem formulation.
result Optimistic Hedge achieves regret bounds, matching upper and lower bounds.
Safe linear bandits over unknown polytopes avoid safety violations and suboptimal actions.
problem Online approach to linear programming with unknown constraints and risks.
method Doubly-optimistic strategy (DOSS) for safe linear bandits over polytopes.
result DOSS achieves tight bounds on efficacy regret and safety violations.
New algorithm optimizes online decision-making with dynamically generated actions.
problem Balancing action generation costs with optimal decision-making in online learning.
method Doubly-optimistic algorithm using LCB for action selection and UCB for action generation.
result Achieves optimal regret bound of .
In many cases an intelligent agent may want to learn how to mimic a single observed demonstrated trajectory. In this work we consider how to perform such procedural learning from observation, which could help to enable agents to better use the enormous set of video data on observation sequences. Our approach exploits t…
New framework for reinforcement learning with sporadic state observations.
problem Partial observability in reinforcement learning.
method Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs).
result Optimistic algorithm achieving regret bound for episodic learning.
Proposes H-UCRL for efficient model-based RL with sublinear regret.
problem Greedy policy exploration in model-based RL ignores epistemic uncertainty.
method Reparameterizes plausible models, hallucinates control, augments input space, solves with greedy planners.
result H-UCRL achieves provably sublinear regret for Gaussian Process models.
Accelerates policy optimization in RL with optimistic and adaptive updates.
problem Improving policy optimization methods in reinforcement learning.
method Integrates foresight into policy improvement step via optimistic and adaptive updates.
result Designs an optimistic policy gradient algorithm, adaptive via meta-gradient learning.
A new framework for structured bandits using influence diagrams and variational Thompson sampling.
problem Complex statistical dependencies in structured bandit problems.
method Influence diagram framework, variational Thompson sampling, tracking structured posterior distribution.
result Empirically evaluated algorithms perform as well as or better than existing baselines.
We consider the problem of online planning in a Markov Decision Process when given only access to a generative model, restricted to open-loop policies - i.e. sequences of actions - and under budget constraint. In this setting, the Open-Loop Optimistic Planning (OLOP) algorithm enjoys good theoretical guarantees but is …
Optimized Q-learning reduces regret in state-aggregated MDPs.
problem Reducing regret in reinforcement learning with state aggregation.
method Optimistic Q-learning applied to fixed-horizon episodic MDPs with aggregated states.
result Regret bound of , independent of states and actions.
New batch RL method avoids overly optimistic policies.
problem Challenges in applying RL to large domains with limited data.
method Modified Bellman optimality and evaluation back-up for conservative updates.
result Can find approximately best policies within explored state-action space.
Optimistic Thompson Sampling reduces regret in unknown multi-player games.
problem Navigating uncertainty in unknown multi-player games with strategic decision-making.
method Introduces Thompson Sampling algorithms that exploit opponents' actions and reward structures.
result Achieves over tenfold improvements in experimental budgets with logarithmic regret bound.
Algorithm converges to Nash equilibria in competitive games.
problem Finding Nash equilibria in decentralized, competitive Markov games.
method Decentralized Optimistic Gradient Descent/Ascent with a critic.
result Converges to the set of Nash equilibria under self-play.
Actor-critic methods, a type of model-free Reinforcement Learning, have been successfully applied to challenging tasks in continuous control, often achieving state-of-the art performance. However, wide-scale adoption of these methods in real-world domains is made difficult by their poor sample efficiency. We address th…
New algorithm reduces reinforcement learning regret to sqrt(d^3T).
problem Efficient reinforcement learning with generalized linear function approximation.
method Optimistic closure assumption for generalized linear functions.
result Proved regret bound of O(sqrt(d^3T)).
Adaptive discretization improves model-based RL in large spaces.
problem Efficient model-based reinforcement learning in large state-action spaces.
method Optimistic one-step value iteration with adaptive discretization.
result Adaptive discretization leads to better performance and lower memory usage.
Bandits roaming Hilbert spacequant-ph
The thesis optimizes quantum state exploration using bandit algorithms.
problem Maximizing reward in online learning of quantum state properties.
method Multi-armed bandit approach to select observables, minimizing regret.
result Optimal strategies with matching upper and lower bounds for regret.
New principle optimizes bandit decisions with context.
problem Dealing with general function classes and large context spaces in bandits.
method Upper Counterfactual Confidence Bounds (UCCB) principle.
result Proves optimality and efficiency in complex settings.
Optimistic Q-learning reduces sample complexity for systems with known dynamics.
problem Sample efficiency in reinforcement learning with partial dynamics knowledge.
method Optimistic Q-learning algorithm for systems with additive disturbance model.
result Achieves regret under perfect knowledge of dynamics.
New findings on universal learning in contextual bandits with adversarial rewards.
problem Learning in contextual bandits with time-varying, adversarial rewards.
method Characterization of learnable processes and necessary/sufficient conditions for universal learning.
result Optimistic universal learning for contextual bandits with adversarial rewards is impossible in general.
New algorithm achieves consistent learning from context in bandit problems.
problem Learning from context in bandit problems with non-i.i.d. contexts.
method Optimistically universal learning rule balancing generalization and personalization.
result Achieves universal consistency for large classes of non-i.i.d. contexts.
RandQL is a new model-free algorithm for MDPs with a novel learning rate randomization approach.
problem Minimizing regret in episodic MDPs with model-free methods.
method Randomized Q-learning with learning rate randomization.
result RandQL achieves optimal regret bounds in both tabular and metric state-action spaces.
Improved POMDP regret to sqrt(T) with known observation model.
problem Average-reward POMDPs with unknown transition model but known observation model.
method Optimistic algorithm using deterministic policies and novel estimation techniques.
result First approach with regret guarantee of sqrt(T) against optimal policy.
Bayesian classifier improves robustness with optimistic score ratio.
problem Limited information on class-conditional distribution.
method Optimistic score ratio for robust binary classification.
result Bayesian classifier using optimistic score ratio is robust and computationally tractable.
We show that the optimistic limits of the colored Jones polynomials of the hyperbolic knots coincide with the optimistic limits of the Kashaev invariants modulo .
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guaranteesstat.ML
OPSRL algorithm reduces regret with few samples in reinforcement learning.
problem High regret in reinforcement learning with limited data.
method Optimistic Posterior Sampling (OPSRL) with logarithmic sample complexity.
result Guaranteed high-probability regret bound of .
The optimistic limit is the mathematical formulation of the classical limit which is a physical method to expect the actual limit by using saddle point method of certain potential function. The original optimistic limit of the Kashaev invariant was formulated by Yokota, and a modified formulation was suggested by the a…
Optimistic estimate predicts best fitting performance of nonlinear models.
problem Evaluating the potential of nonlinear models in fitting.
method Proposes an optimistic estimate to quantify the smallest sample size for fitting nonlinear models.
result Predicts specific subsets of targets that can be fitted at overparameterization.
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RLcs.LG
Optimistic NPG improves policy optimization in online RL with efficient sample complexity.
problem Limited theoretical understanding of policy optimization, especially in online RL.
method Combines natural policy gradient with optimistic policy evaluation.
result Achieves optimal dimension dependence sample complexity for learning near-optimal policies.
We give a simple optimistic algorithm for which it is easy to derive regret bounds of after steps in uniformly ergodic Markov decision processes with states, actions, and mixing time parameter . These bounds are the first regret bounds in the general, non-epi…
Characterizes concept classes for optimistic online learning.
problem Understanding minimal assumptions for online learnability.
method Investigates two questions about concept classes' learnability.
result Characterizes all concept classes for optimistically universal online learnability.
We show how to take any two parameter-free online learning algorithms with different regret guarantees and obtain a single algorithm whose regret is the minimum of the two base algorithms. Our method is embarrassingly simple: just add the iterates. This trick can generate efficient algorithms that adapt to many norms s…
Paper optimizes reinforcement learning in self-play games with reduced steps.
problem Optimizing reinforcement learning algorithms for self-play in two-player zero-sum games.
method Proposes optimistic Nash Q-learning and Nash V-learning algorithms with improved sample complexity.
result Achieves sample complexity of for Nash Q-learning and for Nash V-learning, closing the gap with lower bounds.
Real-world applications require RL algorithms to act safely. During learning process, it is likely that the agent executes sub-optimal actions that may lead to unsafe/poor states of the system. Exploration is particularly brittle in high-dimensional state/action space due to increased number of low-performing actions. …
We study online reinforcement learning for finite-horizon deterministic control systems with {\it arbitrary} state and action spaces. Suppose that the transition dynamics and reward function is unknown, but the state and action space is endowed with a metric that characterizes the proximity between different states and…
New algorithms reduce forecasting errors by leveraging optimistic learning and hinting.
problem Real-time climate forecasting with delayed feedback.
method Developed optimistic online learning algorithms that reduce regret under delayed feedback.
result Optimistic learning algorithms achieve optimal regret guarantees under delayed feedback.
Kernel-UCBVI algorithm balances exploration and exploitation in metric state-action spaces.
problem Exploration-exploitation dilemma in finite-horizon reinforcement learning with metric state-action spaces.
method Kernel-UCBVI, leveraging smoothness and kernel estimators of rewards and transitions.
result First regret bound for kernel-based RL using smoothing kernels, .
Omega method mitigates noise in stochastic game optimization.
problem Noise sensitivity and convergence issues in stochastic game optimization.
method Omega method incorporates EMA of historic gradients in its update rule.
result Omega method outperforms optimistic gradient method in stochastic games.
New method minimizes regret in AMDP with high probability.
problem Pessimistic regret analysis in AMDP.
method Developed a new policy search method achieving optimistic regret.
result Achieved sublinear optimistic regret with high probability.
Optimal algorithm for linear bandits on ellipsoids with minimax regret bound.
problem Linear stochastic bandits on ellipsoids.
method Novel sequential procedure to estimate norm of parameters, followed by an explore-and-commit strategy.
result Regret bound matches minimax lower bound with multiplicative constant.
Unified framework for model- and value-optimistic reinforcement learning.
problem Designing and analyzing algorithms for episodic reinforcement learning.
method Lagrangian duality for a unified view of optimism.
result Unified algorithms with both efficient implementation and probabilistic analysis.
New RL algorithm learns good actions from offline data, reducing uncertainty and divergence.
problem Limited applicability of current RL algorithms in real-world settings due to high costs of exploration.
method Proposes an algorithm for batch RL using a fixed offline dataset, with penalties for policy and value constraints.
result Compared favorably to state-of-the-art methods on 32 continuous-action benchmarks.
It is well known that in stochastic multi-armed bandits (MAB), the sample mean of an arm is typically not an unbiased estimator of its true mean. In this paper, we decouple three different sources of this selection bias: adaptive \emph{sampling} of arms, adaptive \emph{stopping} of the experiment, and adaptively \emph{…
New RL method handles large state-action spaces with complex models.
problem Complex models and large state-action spaces in reinforcement learning.
method π-KRVI, an optimistic modification of least-squares value iteration using kernel ridge regression.
result First order-optimal regret guarantees under general settings, improving over state of the art.
Efficiently solves exploration-exploitation in LQR using Lagrangian relaxation.
problem Exploration-exploitation dilemma in linear quadratic regulator (LQR) setting.
method Relax optimistic optimization into a constrained extended LQR problem, then solve using Riccati equations.
result Computes -optimistic controller efficiently with Riccati equations.