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Found 25 papers, 12 papers with code
Acceleration in Policy Optimization
We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimistic and adaptive updates.
Large-Scale Retrieval for Reinforcement Learning
Effective decision making involves flexibly relating past experiences and relevant contextual information to a novel situation.
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
We consider the offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected return while satisfying given cost constraints, learning only from a pre-collected dataset.
Retrieval-Augmented Reinforcement Learning
In this paper we explore an alternative paradigm in which we train a network to map a dataset of past experiences to optimal behavior.
Policy improvement by planning with Gumbel
AlphaZero is a powerful reinforcement learning algorithm based on approximate policy iteration and tree search.
Muesli: Combining Improvements in Policy Optimization
We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss.
Ranked #8 on
Atari Games
on atari game
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Credit assignment in reinforcement learning is the problem of measuring an action's influence on future rewards.
On the role of planning in model-based deep reinforcement learning
These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.
Model-based Reinforcement Learning
reinforcement-learning
+1
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban
We explore whether integrated tasks like Mujoban can be solved by composing RL modules together in a sense-plan-act hierarchy, where modules have well-defined roles similarly to classic robot architectures.
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
To encourage progress towards this goal we introduce a set of physically embedded planning problems and make them publicly available.
Value-driven Hindsight Modelling
Value estimation is a critical component of the reinforcement learning (RL) paradigm.
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.
Ranked #1 on
Atari Games
on Atari 2600 Alien
Augmenting learning using symmetry in a biologically-inspired domain
Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the natural sciences to reduce the dimensionality of systems of equations.
An investigation of model-free planning
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity.
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
In contrast to off-policy algorithms based on Importance Sampling which re-weight data, CF-GPS leverages a model to explicitly consider alternative outcomes, allowing the algorithm to make better use of experience data.
Learning to Search with MCTSnets
They are most typically solved by tree search algorithms that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree.
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
The game of chess is the most widely-studied domain in the history of artificial intelligence.
Ranked #1 on
Game of Go
on ELO Ratings
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects.
Model-based Reinforcement Learning
reinforcement-learning
+1
The Predictron: End-To-End Learning and Planning
One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning.
Learning values across many orders of magnitude
Most learning algorithms are not invariant to the scale of the function that is being approximated.
Ranked #12 on
Atari Games
on Atari 2600 Centipede
Increasing the Action Gap: New Operators for Reinforcement Learning
Extending the idea of a locally consistent operator, we then derive sufficient conditions for an operator to preserve optimality, leading to a family of operators which includes our consistent Bellman operator.
Ranked #1 on
Atari Games
on Atari 2600 Elevator Action
Deep Reinforcement Learning with Double Q-learning
The popular Q-learning algorithm is known to overestimate action values under certain conditions.
Ranked #10 on
Atari Games
on Atari 2600 Gopher
Bayes-Adaptive Simulation-based Search with Value Function Approximation
Bayes-adaptive planning offers a principled solution to the exploration-exploitation trade-off under model uncertainty.
Better Optimism By Bayes: Adaptive Planning with Rich Models
The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
+1
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search
Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way.
Model-based Reinforcement Learning
reinforcement-learning
+1
