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Found 6 papers, 4 papers with code
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning
Our algorithm improves the agent's focus with useful masks, while its efficient Masker network only adds 0. 2% more parameters to the original structure, in contrast to previous work.
Correcting discount-factor mismatch in on-policy policy gradient methods
The policy gradient theorem gives a convenient form of the policy gradient in terms of three factors: an action value, a gradient of the action likelihood, and a state distribution involving discounting called the \emph{discounted stationary distribution}.
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers
A common setup for a robotic agent is to have two different computers simultaneously: a resource-limited local computer tethered to the robot and a powerful remote computer connected wirelessly.
Autoregressive Policies for Continuous Control Deep Reinforcement Learning
We introduce a family of stationary autoregressive (AR) stochastic processes to facilitate exploration in continuous control domains.
Benchmarking Reinforcement Learning Algorithms on Real-World Robots
The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simulated benchmark tasks.
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.
