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Found 4 papers, 1 papers with code
Bigger, Better, Faster: Human-level Atari with human-level efficiency
We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark.
Ranked #1 on
Atari Games 100k
on Atari 100k
The Barbados 2018 List of Open Issues in Continual Learning
We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments.
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
Our first contribution is a new policy evaluation algorithm called Distributional Retrace, which brings multi-step off-policy updates to the distributional reinforcement learning setting.
Ranked #6 on
Atari Games
on Atari 2600 Crazy Climber
Sketch-Based Linear Value Function Approximation
Unfortunately, the typical use of hashing in value function approximation results in biased value estimates due to the possibility of collisions.
