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Found 15 papers, 11 papers with code
Hierarchical Imitation Learning for Stochastic Environments
However, such methods are often inappropriate for stochastic environments where the agent must also react to external factors: because agent types are inferred from the observed future trajectory during training, these environments require that the contributions of internal and external factors to the agent behaviour are disentangled and only internal factors, i. e., those under the agent's control, are encoded in the type.
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving
Recent methods addressing this problem typically differentiate through time in a particle filter, which requires workarounds to the non-differentiable resampling step, that yield biased or high variance gradient estimates.
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator.
Communicating via Markov Decision Processes
We contribute a theoretically grounded approach to MCGs based on maximum entropy reinforcement learning and minimum entropy coupling that we call MEME.
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing
Recent research has shown that graph neural networks (GNNs) can learn policies for locomotion control that are as effective as a typical multi-layer perceptron (MLP), with superior transfer and multi-task performance (Wang et al., 2018; Huang et al., 2020).
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
They also allow practitioners to inject biases encoded in the structure of the input graph.
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep.
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning
Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments.
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
We discuss those differences and propose modifications to existing regularization techniques in order to better adapt them to RL.
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning.
Multitask Soft Option Learning
We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference.
Deep Variational Reinforcement Learning for POMDPs
Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown.
Tighter Variational Bounds are Not Necessarily Better
We provide theoretical and empirical evidence that using tighter evidence lower bounds (ELBOs) can be detrimental to the process of learning an inference network by reducing the signal-to-noise ratio of the gradient estimator.
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions.
Auto-Encoding Sequential Monte Carlo
We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models.
