CURIOUS: Intrinsically Motivated Multi-Task, Multi-Goal Reinforcement Learning

By Cédric Colas, Pierre Fournier, Olivier Sigaud, Pierre-Yves Oudeyer. 2018

41 citations recorded by the source corpus.

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Citations in the corpus

DisCoRL: Continual Reinforcement Learning via Policy Distillation (2019, methodology): “This approach is extended in the CURIOUS algorithm [Colas et al., 2018].”
Efficient Exploration via State Marginal Matching (2019, methodology): “Most closely related to our work are methods that perform exploration in the space of states or goals (Colas et al., 2018; Held et al., 2017; Nair et al., 2018; Pong et al., 2019; Hazan et al., 2018).”
Grounding Language to Autonomously-Acquired Skills via Goal Generation (2021, unknown): “Stacking blocks is one of the earliest benchmarks in artiﬁcial intelligence (e.g. Sussman (1973); Tate et al. (1975)) and has led to many simulation and robotics studies (Deisenroth et al., 2011; Xu et al., 2018; Colas et al., 2019a).”
On Reward-Free Reinforcement Learning with Linear Function Approximation (2020, unknown): “…Bellemare et al., 2016, Houthooft et al., 2016, Tang et al., 2017, Florensa et al., 2017, Pathak et al., 2017, Tang et al., 2017, Achiam et al., 2017, Hazan et al., 2018, Burda et al., 2018, Colas et al., 2018, Co-Reyes et al., 2018, Nair et al., 2018, Eysenbach et al., 2018, Pong et al., 2019].”
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics (2020, unknown): “The features of an MDP representation constrains the kind of learning algorithms that can be used: a small and discrete set of actions and states facilitates an exhaustive exploration to discover the most relevant policy [3], while a large dimension and continuous state and action spaces raise explo”
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning (2017, methodology): “Also, using a population-based IMGEP architecture can help bootstrap a deep RL agent (Colas et al., 2018).”
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning (2019, methodology): “Some prior methods propose to choose goals based on heuristics such as learning progress (Baranes & Oudeyer, 2012; Veeriah et al., 2018; Colas et al., 2018a), how offpolicy the goal is (Nachum et al., 2018), or level of difficulty (Florensa et al., 2018b).”