← Authors

Christopher Amato

27 papers · 2013–2026 · 8 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (8) πŸƒ Academic Marathon (12) 🐝 Cross-Pollinator (4) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (30) πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (90) ⚑ Prolific Year (5) πŸ’Ž Century Club (26) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (9)

Conferences

CORL (6) IJCAI (6) AAAI (4) ICML (4) NIPS (4) ICLR (1) RSS (1) UAI (1)

Top co-authors

Andrea Baisero (5) Robert Platt (4) Brett Daley (4) Yuchen Xiao (4) Hai Huu Nguyen (3) Jonathan P. How (3) Dian Wang (2) François Charpillet (2) Joshua Hoffman (2) Jilles Steeve Dibangoye (2)

Keywords

multi-agent reinforcement learning (6) partial observability (5) reinforcement learning (4) partially observable markov decision process (3) deep reinforcement learning (3) multi-robot system (2) agent coordination (2) eligibility trace (2) decentralized partially observable markov decision process (2) multi-agent system (2) policy optimization (1) importance sampling (1) value function (1) policy search (1) robot learning (1) imitation learning (1) policy learning (1) state estimation (1) knowledge transfer (1) policy iteration (1)

Papers

LLM Collaboration with Multi-Agent Reinforcement Learning AAAI 2026
Adversarial Inception Backdoor Attacks against Reinforcement Learning ICML 2025
Leveraging Mutual Information for Asymmetric Learning under Partial Observability CORL 2024
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents NIPS 2024
Improving Deep Policy Gradients with Value Function Search ICLR 2023
Equivariant Reinforcement Learning under Partial Observability CORL 2023
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning ICML 2023
Leveraging Fully Observable Policies for Learning under Partial Observability CORL 2022
Asymmetric DQN for partially observable reinforcement learning UAI 2022
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning AAAI 2022
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning NIPS 2022
Shield Decentralization for Safe Multi-Agent Reinforcement Learning NIPS 2022
Reconciling Rewards with Predictive State Representations IJCAI 2021
Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps CORL 2020
Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability CORL 2020
Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract) AAAI 2020
Reconciling Ξ»-Returns with Experience Replay NIPS 2019
Macro-Action-Based Deep Multi-Agent Reinforcement Learning CORL 2019
Learning to Teach in Cooperative Multiagent Reinforcement Learning AAAI 2019
Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning IJCAI 2018
COG-DICE: An Algorithm for Solving Continuous-Observation Dec-POMDPs IJCAI 2017
Learning in POMDPs with Monte Carlo Tree Search ICML 2017
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability ICML 2017
Stick-Breaking Policy Learning in Dec-POMDPs IJCAI 2015
Policy Search for Multi-Robot Coordination under Uncertainty RSS 2015
Exploiting Separability in Multiagent Planning with Continuous-State MDPs (Extended Abstract) IJCAI 2015
Optimally Solving Dec-POMDPs as Continuous-State MDPs IJCAI 2013