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Found 12 papers, 2 papers with code
Iterative Option Discovery for Planning, by Planning
Discovering useful temporal abstractions, in the form of options, is widely thought to be key to applying reinforcement learning and planning to increasingly complex domains.
The Benefits of Model-Based Generalization in Reinforcement Learning
First, we provide a simple theorem motivating how learning a model as an intermediate step can narrow down the set of possible value functions more than learning a value function directly from data using the Bellman equation.
Model-based Reinforcement Learning
reinforcement-learning
+1
Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions
However, Asynchronous VI still requires a maximization over the entire action space, making it impractical for domains with large action space.
Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units
We then show how HNCA can be extended to optimize a more general function of the outputs of a network of stochastic units, where the function is known to the agent.
Hindsight Network Credit Assignment
We present Hindsight Network Credit Assignment (HNCA), a novel learning method for stochastic neural networks, which works by assigning credit to each neuron's stochastic output based on how it influences the output of its immediate children in the network.
Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning
We demonstrate analytically and experimentally that such pathological behaviours can impact a wide range of RL and dynamic programming algorithms; such behaviours can arise both with and without bootstrapping, and with linear function approximation as well as with more complex parameterized functions like neural networks.
Variance Reduced Advantage Estimation with $δ$ Hindsight Credit Assignment
Hindsight Credit Assignment (HCA) refers to a recently proposed family of methods for producing more efficient credit assignment in reinforcement learning.
MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments
With the representation learning problem simplified, we can perform experiments with significantly less computational expense.
Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control
Finally, we apply Metatrace for control with nonlinear function approximation in 5 games in the Arcade Learning Environment where we explore how it impacts learning speed and robustness to initial step-size choice.
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods
This paper investigates estimating the variance of a temporal-difference learning agent's update target.
A Reverse Hex Solver
We present Solrex, an automated solver for the game of Reverse Hex. Reverse Hex, also known as Rex, or Misere Hex, is the variant of the game of Hex in which the player who joins her two sides loses the game.
Neurohex: A Deep Q-learning Hex Agent
DeepMind's recent spectacular success in using deep convolutional neural nets and machine learning to build superhuman level agents --- e. g. for Atari games via deep Q-learning and for the game of Go via Reinforcement Learning --- raises many questions, including to what extent these methods will succeed in other domains.
