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Found 16 papers, 5 papers with code
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
In this work, we present Conditional Adversarial Latent Models (CALM), an approach for generating diverse and directable behaviors for user-controlled interactive virtual characters.
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
As communication protocols evolve, datacenter network utilization increases.
Towards Autonomous Grading In The Real World
As the heuristics are capable of successfully solving the task in the simulated environment, we show they can be leveraged to guide a learning agent which can generalize and solve the task both in simulation and in a scaled prototype environment.
Ensemble Bootstrapping for Q-Learning
Finally, We demonstrate the superior performance of a deep RL variant of EBQL over other deep QL algorithms for a suite of ATARI games.
Reinforcement Learning for Datacenter Congestion Control
We approach the task of network congestion control in datacenters using Reinforcement Learning (RL).
Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons
In reinforcement learning, the discount factor $\gamma$ controls the agent's effective planning horizon.
Language is Power: Representing States Using Natural Language in Reinforcement Learning
Recent advances in reinforcement learning have shown its potential to tackle complex real-life tasks.
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning
In recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains.
Contextual Inverse Reinforcement Learning
In this setting, the reward, which is unknown to the agent, is a function of a static parameter referred to as the context.
Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients
In recent years, advances in deep learning have enabled the application of reinforcement learning algorithms in complex domains.
Inverse Reinforcement Learning in Contextual MDPs
Most importantly, we show both theoretically and empirically that our algorithms perform zero-shot transfer (generalize to new and unseen contexts).
Distributional Policy Optimization: An Alternative Approach for Continuous Control
We show that optimizing over such sets results in local movement in the action space and thus convergence to sub-optimal solutions.
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces
We propose a computationally efficient algorithm that combines compressed sensing with imitation learning to solve text-based games with combinatorial action spaces.
Action Robust Reinforcement Learning and Applications in Continuous Control
In this work we formalize two new criteria of robustness to action uncertainty.
A Deep Hierarchical Approach to Lifelong Learning in Minecraft
Skill distillation enables the HDRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network.
