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Found 10 papers, 4 papers with code
Unlocking the Power of Representations in Long-term Novelty-based Exploration
We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space.
Human-level Atari 200x faster
The task of building general agents that perform well over a wide range of tasks has been an importantgoal in reinforcement learning since its inception.
Revisiting Peng's Q($λ$) for Modern Reinforcement Learning
These results indicate that Peng's Q($\lambda$), which was thought to be unsafe, is a theoretically-sound and practically effective algorithm.
Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning
We introduce Behavior Transfer (BT), a technique that leverages pre-trained policies for exploration and that is complementary to transferring neural network weights.
Temporal Difference Uncertainties as a Signal for Exploration
Instead, we incorporate it as an intrinsic reward and treat exploration as a separate learning problem, induced by the agent's temporal difference uncertainties.
Agent57: Outperforming the Atari Human Benchmark
Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade.
Ranked #1 on
Atari Games
on Atari 2600 HERO
Value-driven Hindsight Modelling
Value estimation is a critical component of the reinforcement learning (RL) paradigm.
Never Give Up: Learning Directed Exploration Strategies
Our method doubles the performance of the base agent in all hard exploration in the Atari-57 suite while maintaining a very high score across the remaining games, obtaining a median human normalised score of 1344. 0%.
Ranked #7 on
Atari Games
on atari game
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.
Recurrent Experience Replay in Distributed Reinforcement Learning
Using a single network architecture and fixed set of hyperparameters, the resulting agent, Recurrent Replay Distributed DQN, quadruples the previous state of the art on Atari-57, and surpasses the state of the art on DMLab-30.
Ranked #1 on
Atari Games
on Atari 2600 Pong
