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Found 9 papers, 4 papers with code
Estimating Q(s,s') with Deterministic Dynamics Gradients
In this paper, we introduce a novel form of a value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.
Vision-Language Models as a Source of Rewards
Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning.
In-context Reinforcement Learning with Algorithm Distillation
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model.
Hard Attention Control By Mutual Information Maximization
We also show that the agent's internal representation of the surroundings, a live mental map, can be used for control in two partially observable reinforcement learning tasks.
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
In this paper, we introduce a novel form of value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
In this work, we show how visual trajectories can be hallucinated to appear successful by altering agent observations using a generative model trained on relatively few snapshots of the goal.
Imitating Latent Policies from Observation
In this paper, we describe a novel approach to imitation learning that infers latent policies directly from state observations.
Learning to Compose Skills
We present a differentiable framework capable of learning a wide variety of compositions of simple policies that we call skills.
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning
To address this issue, we develop a framework through which a deep RL agent learns to generalize policies from smaller, simpler domains to more complex ones using a recurrent attention mechanism.
