About
The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.
( Image credit: Playing Atari with Deep Reinforcement Learning )
Benchmarks
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Subtasks
Datasets
Latest papers without code
Combining Off and On-Policy Training in Model-Based Reinforcement Learning
Recently, MuZero demonstrated that it is possible to master both Atari games and board games by directly learning a model of the environment, which is then used with MCTS to decide what move to play in each position.
Return-Based Contrastive Representation Learning for Reinforcement Learning
Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL).
Q-Value Weighted Regression: Reinforcement Learning with Limited Data
QWR is an extension of Advantage Weighted Regression (AWR), an off-policy actor-critic algorithm that performs very well on continuous control tasks, also in the offline setting, but has low sample efficiency and struggles with high-dimensional observation spaces.
Learning State Representations from Random Deep Action-conditional Predictions
In this work, we study auxiliary prediction tasks defined by temporal-difference networks (TD networks); these networks are a language for expressing a rich space of general value function (GVF) prediction targets that may be learned efficiently with TD.
Measuring Progress in Deep Reinforcement Learning Sample Efficiency
Sampled environment transitions are a critical input to deep reinforcement learning (DRL) algorithms.
An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios
Intelligent robots provide a new insight into efficiency improvement in industrial and service scenarios to replace human labor.
Hierarchical Width-Based Planning and Learning
In classical planning, we show how IW(1) at two levels of abstraction can solve problems of width 2.
Deep Reinforcement Learning with Quantum-inspired Experience Replay
In this paper, a novel training paradigm inspired by quantum computation is proposed for deep reinforcement learning (DRL) with experience replay.
Unsupervised Active Pre-Training for Reinforcement Learning
On DMControl suite, APT beats all baselines in terms of asymptotic performance and data efficiency and dramatically improves performance on tasks that are extremely difficult for training from scratch.
Deep Q-Learning with Low Switching Cost
We initiate the study on deep reinforcement learning problems that require low switching cost, i. e., small number of policy switches during training.
ATARI GAMES Q-LEARNING RECOMMENDATION SYSTEMS REPRESENTATION LEARNING