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Found 11 papers, 5 papers with code
Exploration via Epistemic Value Estimation
We propose epistemic value estimation (EVE): a recipe that is compatible with sequential decision making and with neural network function approximators.
Chaining Value Functions for Off-Policy Learning
To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can learn `off-policy' about policies that differ from the policy used to generate its experience.
A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder
We quantified the theoretical energy required for each patient and time-point to reach a symptom-free state given individual symptom-network topology (E 0 ) and 1) tested if E 0 predicts future symptom improvement and 2) whether this relationship is moderated by Polygenic Risk Scores (PRS) of mental disorders, childhood maltreatment experience, and self-reported resilience.
Learning and Planning in Complex Action Spaces
Instead, only small subsets of actions can be sampled for the purpose of policy evaluation and improvement.
Ranked #1 on
Continuous Control
on acrobot.swingup
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
AlgebraNets
Neural networks have historically been built layerwise from the set of functions in ${f: \mathbb{R}^n \to \mathbb{R}^m }$, i. e. with activations and weights/parameters represented by real numbers, $\mathbb{R}$.
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
Gated Linear Networks
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs).
Off-Policy Actor-Critic with Shared Experience Replay
We investigate the combination of actor-critic reinforcement learning algorithms with uniform large-scale experience replay and propose solutions for two challenges: (a) efficient actor-critic learning with experience replay (b) stability of off-policy learning where agents learn from other agents behaviour.
Ranked #5 on
Atari Games
on Atari-57
Multi-task Deep Reinforcement Learning with PopArt
This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on.
Ranked #1 on
Visual Navigation
on Dmlab-30
Kickstarting Deep Reinforcement Learning
Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance.
