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Found 8 papers, 3 papers with code
Thinker: Learning to Plan and Act
This approach eliminates the need for handcrafted planning algorithms by enabling the agent to learn how to plan autonomously and allows for easy interpretation of the agent's plan with visualization.
Unbiased Weight Maximization
Notably, to our knowledge, this is the first learning rule for a network of Bernoulli-logistic units that is unbiased and scales well with the number of network's units in terms of learning speed.
Structural Credit Assignment with Coordinated Exploration
The first category includes algorithms that enable coordinated exploration among units, such as MAP propagation.
Domain Generalization for Robust Model-Based Offline Reinforcement Learning
Existing offline reinforcement learning (RL) algorithms typically assume that training data is either: 1) generated by a known policy, or 2) of entirely unknown origin.
Turing Completeness of Bounded-Precision Recurrent Neural Networks
Previous works have proved that recurrent neural networks (RNNs) are Turing-complete.
Learning by Competition of Self-Interested Reinforcement Learning Agents
The high variance arises from the inefficient structural credit assignment since a single reward signal is used to evaluate the collective action of all units.
MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents
An alternative way of training an artificial neural network is through treating each unit in the network as a reinforcement learning agent, and thus the network is considered as a team of agents.
Reinforcement Learning with Feedback-modulated TD-STDP
Spiking neuron networks have been used successfully to solve simple reinforcement learning tasks with continuous action set applying learning rules based on spike-timing-dependent plasticity (STDP).
