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Found 13 papers, 8 papers with code
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games
Video games have served as useful benchmarks for the decision making community, but going beyond Atari games towards training agents in modern games has been prohibitively expensive for the vast majority of the research community.
Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments
Motivated by the recent success of Offline RL and Imitation Learning (IL), we conduct a study to investigate whether agents can leverage offline data in the form of trajectories to improve the sample-efficiency in procedurally generated environments.
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
In this work, we propose ensemble value functions for multi-agent exploration (EMAX), a general framework to seamlessly extend value-based MARL algorithms with ensembles of value functions.
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
We envision a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse.
Deep Reinforcement Learning for Multi-Agent Interaction
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
We show that a team of agents is able to adapt to novel tasks when provided with task embeddings.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning
Learning control from pixels is difficult for reinforcement learning (RL) agents because representation learning and policy learning are intertwined.
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking
We categorize the methods based on how they adapt the action: action replacement, action projection, and action masking.
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning
Reinforcement learning (RL) algorithms are often categorized as either on-policy or off-policy depending on whether they use data from a target policy of interest or from a different behavior policy.
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
Deep reinforcement learning (RL) agents that exist in high-dimensional state spaces, such as those composed of images, have interconnected learning burdens.
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
Intrinsic rewards can improve exploration in reinforcement learning, but the exploration process may suffer from instability caused by non-stationary reward shaping and strong dependency on hyperparameters.
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult.
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards.
