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Found 7 papers, 3 papers with code
Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation
Existing off-policy reinforcement learning algorithms typically necessitate an explicit state-action-value function representation, which becomes problematic in high-dimensional action spaces.
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state.
MP3: Movement Primitive-Based (Re-)Planning Policy
We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3).
Joint Representations for Reinforcement Learning with Multiple Sensors
Combining inputs from multiple sensor modalities effectively in reinforcement learning (RL) is an open problem.
Deep Black-Box Reinforcement Learning with Movement Primitives
In this paper, we present a new algorithm for deep ERL.
ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives
MPs can be broadly categorized into two types: (a) dynamics-based approaches that generate smooth trajectories from any initial state, e. g., Dynamic Movement Primitives (DMPs), and (b) probabilistic approaches that capture higher-order statistics of the motion, e. g., Probabilistic Movement Primitives (ProMPs).
Differentiable Trust Region Layers for Deep Reinforcement Learning
However, enforcing such trust regions in deep reinforcement learning is difficult.
